{"type":"session","version":3,"id":"7ae2eb7c-0d51-47ee-bc31-4031c0d02c31","timestamp":"2026-04-11T20:56:09.575Z","cwd":"/home/alex/.pi/agent/skills/frontend"}
{"type":"agent_start"}
{"type":"turn_start"}
{"type":"message_start","message":{"role":"user","content":[{"type":"text","text":"IMPORTANT: This is a real scenario. Choose which existing frontend skill or skills to load from this directory. Inspect the skill folders if needed. User request: \"Our cards repeat the same pattern everywhere and the tokens drifted from the design system. Extract reusable pieces and bring things back into alignment.\" Return exactly three lines: Skills: ...\\nOrder: ...\\nWhy: ..."}],"timestamp":1775940969867}}
{"type":"message_end","message":{"role":"user","content":[{"type":"text","text":"IMPORTANT: This is a real scenario. Choose which existing frontend skill or skills to load from this directory. Inspect the skill folders if needed. User request: \"Our cards repeat the same pattern everywhere and the tokens drifted from the design system. Extract reusable pieces and bring things back into alignment.\" Return exactly three lines: Skills: ...\\nOrder: ...\\nWhy: ..."}],"timestamp":1775940969867}}
{"type":"message_start","message":{"role":"assistant","content":[],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_start","contentIndex":0,"partial":{"role":"assistant","content":[{"type":"thinking","thinking":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_end","contentIndex":0,"content":"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_start","contentIndex":1,"partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{},"partialJson":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{},"partialJson":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"{\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"jgYdn95cVVHnlK15jThXKFDi33TIiftGgJ3KsFCBxHXEsahrFLdD0FasGJUgNCkeOcxeIY69Uov0UFzm14w6bXzDtcojyk6Ag5VQhaFL5qlN7jIx+PoYas6+44wNoADbeprx/QgMXqpVM/npNx2Q2/TAy6+olAHJpjQzDDRUpoXUDTl4td0FQnENvJHPwBfopfdChLXxFLVrgba2DHQFS0bkhAcTwVRDdB6oKVta0Y137lB78cQNbLREd/NCSIdbsRIKJb4NMH80a3ZOt4W3ptSWrKjKBMLP0oz9jf+y/PYV1dpXNoJZDv+wz2dBRpBtGvurkUuGr8UWn6PVGJffthMHo4b+OhAPgCb777bzFlwoHUccvES4fKDQxVSrxKNU7ekg3FBZgfU95Bl/vv9xj9+R7aynkFZLVIGke09zJTut0mXYdolBaX7Hjkt2zcad2lPoSdgRz9QYEzlOM/8T5Mi1ynB0k4FTepTHagkWs+fbmvbNPnjIozJFxkuLt1BvCNnJOu6d+5NOIYJZoHLRUHlXJHaqrsaBDMfTWUX5XG0V3BT37SzT+XzPdcWYpf49+FOyedA70P3fluswT70Xo934zdQuRTa5tI+vgqZe9RroF2RTwoNKPsO6qwve9B+q9gzFoVERsQVyuqHBrZxkdSKRNSg0Lik/pNMltRuadVt5UrMG8SFv3ykj7VCv7qdGR3BBnHkQhLgWjnhVko1inlU2cWwYteoQef+2ZSi02dQy2ZqyPHyfNeduwADFf6J/+su25Dh8eeZNyFiJWmgEBBQIMJXmqmEl21XnFst8Np+P2Oh2x1XXye+Qj4k3xUYeCCYjIhHa1XaIsCIoJyOvbK48iLwmsDYObz77Gqy6HgDCDN9ShF9qHWIfmnzULaGtQjb6+0IxfRcBPOl7dA50fX1ln5ECqDrNxgX8beY86aHLhh2kGk6Su7oZ+cWbeJQ7PHnITmdysrk7x058m1WaueFqhIOEIkhKng5Fl1VonaoMPmf8OsiKTE05IN+w2OuMzt+mp7jWZ7R/CoWgD8hQrR2L46tYd00sm02w8VNBfLQx9B9Yajvyyq70jVfB6TWEcv1h83AEPqCNdgAMB2/MG02ph3Fqa8E/91cdwc7hmDPKJZ7oqwpMj/eBgt/QBRe5CrWa2yG/pSJmLPN15XHTof4VPS9UHJ3Bj3b07d8pDFEchD2Od3U7+wNm6ukloeKYk58G2HWnPNAUxIhqx8jYrbW9YcpD81VJVuQkHjGpe10r0A22UbeKY1DJXOXEywZVyuA4tW8aW7q58CD3MbBv0xk39UkBW7EKU8a+fPm5xKKy9js0b7Y5J/JhVIED27hUmfj4aoTOdn/tUXf5N8xnc3PcJru3BlK++II61nK0Dx10shS3IvCAJXtLJQVtdnGZj7G1pfs8pspxgC5Kru5wHG3jly2CNOWBQERJRnMTqFlyj8UxdTAIn2WEC0vXj4QHpYZZqQZpzYr3REQxy8uHghw1/FbUHy63z1Z31SOdzfUQUi/DPYir8juTagYq85ZV1/xGmGL6LL38+yiOqKs93pQuWLfD3juNk+Ml9rLsUD/vMit7iEDc2c2vH7Vrcb3r6bYiSX3J7oQXEJx0qq8TdanC97SnpPToEeQWmm5qnEsotzrJTnWA/Q6DD9ixi8Z0WLRrnvW+DA1lEhVho7z2Hu9ltZOEI/qhjVR2luuxVEZU4mOKEaF8EUKfdt8nK7puRx1/yM8fdso11/GFyrcwkj//REJ1+Avpct2bo8QPzu9z6hWHlQqp+1YEX/iHUaT/8ppYAkYIE0YYpefomD2j2SF4aetB7UAV17yMEKWt+NzGEEV5zrNMyGSWh/J3N0Bqd4LMPuQmgVA1MMBB07O0qr1/jKIBSXOLzE99DXEgyGVXALAXz4ED8WDBsZCG/P6hoPStrdcs1CJwMCs1hV676ZJxsY6H4jDS7reQPOZufr/MNN6RkZKgSf58B+d8EwEN8IZfIWgfT8wASEkSgAnYH2xAN5I8KDNGhPOwyQ/IZQTZklJHPjg45mJYk0JFABekgF7FPllVjQH8yPXiUhJEtxvxXGbamoR8JTxIj5efM7rmuwSJYf2P85hhcUGtSMAjOCkpKD48K6KXzcLGoOshw1hYozdg01fHInkMOzBJ+J85vlxF2n/wRDsxJZXta/3WkyrA7Wafj396idKmomX0FnaloEoapgNbCvBvbjtqGhxs50LfAQRNTuO1d9ZBx4KSklpwcJf/Y6xcKUeHgIDczuny2Hpq1svj6xyDhC+1MEKN6bs9kuko59T4Y1NBlrlC1h+qZt8IrHPl4L08rd+q/gurWCtbQ0WNvWQU8RWwXpf4rsax5+A9+mN3P1nje0TQybrWBGG46LvIrJW9xZBUrkAoJONdFliwXftVmLzjsziJfK3flEe8eXPRIwDZBiWmTLs/YJSFShiI7ReM/VS5FgQc1Y8x9OBFbyOeCAYFu8ynGabJnIbDKdmEjOaC5TFE+k1jkYgo7ZAHUt6zJtgwHaSDbmJAvqqqHE+8Kx7zAMvdxsUGNQdIFWUs74kdciX/Iz/gLHvM9LUVTkkXs6lrSF85rAtipkOiD7ZDcf4lkBS57PBXV/QmqbnjEq5O5QC6P+X+UDw7tsuc4ESwcYMOYIEUQCNEBnyQyWsz3pLP5UpfSwW3GS9vQtIqArXG9Nd9UJu+Yn4L/NuIBW58q+BTlZJ8yiUEvIv+oPqB3XWtyhZCrTFnpdptoe7yld/GL1+1YBgr2BLW4rJO8M5hfjL/f+0ZU1JbX+PR0dt0ojbLAOD7u0J/Grs/BPUiU4r18WuKJxbv+gF9wR5/TKWSxusJmDz2KE6TsFlDco389Buu3ThWSH/7rp6wbW2Tks6GJvSoL4a5aNIgw0Eryk0hcDxjtrbk0V+EtPifTJeZTRAd+A/xP55DOSirrivPOC4rE9sERVkhiEt5oX1wP7FKxiCUFd8blVgQDb2LJU99wJ7LuK7P85slNP3bg6vdvc5E+DGBlHc7Q3WrRKGbkbXsFYO8Exf+bd5FRvTPF/AKrkxrJYdnHmaXE7PeIdo/Y4hLbUvUMtqdMo1qQmsWWyeKPF2McNaW+Gatwoqdqrjm4jSV2kSnfBWYE/pqUsrSUFUTOd/lAQtqKMQRsiLgJEvuig7sEfGIelEopfS5l5rXJYd1GkUf3JQVi/CtRqtmTQ7iTyeUs1aNvcS79pSxXXwAkl7j2uFX/TdE+SLlWVRT16dHJ3z5L1Qkyi9hPFALahObY2bPfUsPHbgO40yn96YqW4GpVoSMdh34YTc8guvHSHcrob+JidHjMranUF4lks+aieHF8Cn6UfnDbHs/cHEkXi7lDfYV0xRaU4rfAjq5ULPe53J17pRQv5K7JKjKW3dRXpzA4wPY7Iyi4T28PgRD/BV3mRC5ct+3p+y7vd7f/kMFtfjZ/EFnqX0ENkr94fO6ra0LPfYIRduS8t1aE3+qmX1gG61rOPu+BE7h94Uhx5HNTOqkN610bOF85Yf+Ne2UIXyg+zopKy4044Jox6XT6bcGeLKqvcYWKYScVPZQThk9wHrhzq0KouOAZRtDdozvXY0mVEbKE2eH58/tmFfcuEUurGwYjkx0odhIQxKWP0x4D1PAQ4Wb3NEWw3JZBJU1UTgLvR7VRGJ+V0C0FnOTEGh8QnAEsJlSLOQUCxvU012njngqzkRk6GE2lc1qaktmRLF69nWt3VlPjjPQaDiod1ekIxfhb69+fK9gFK98g6Xb1OxU1TClx4JbEGj7YS/OqlisKeXlBuxaqpaOTao6IjtTiGQ7NLwHiOX3zrzx27DyLgO9\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"path","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{},"partialJson":"{\"path"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{},"partialJson":"{\"path"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"\":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{},"partialJson":"{\"path\":"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{},"partialJson":"{\"path\":"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"\"/","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/"},"partialJson":"{\"path\":\"/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/"},"partialJson":"{\"path\":\"/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"home","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home"},"partialJson":"{\"path\":\"/home"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home"},"partialJson":"{\"path\":\"/home"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/al","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/al"},"partialJson":"{\"path\":\"/home/al"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/al"},"partialJson":"{\"path\":\"/home/al"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"ex","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex"},"partialJson":"{\"path\":\"/home/alex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex"},"partialJson":"{\"path\":\"/home/alex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/.","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/."},"partialJson":"{\"path\":\"/home/alex/."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/."},"partialJson":"{\"path\":\"/home/alex/."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"pi","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi"},"partialJson":"{\"path\":\"/home/alex/.pi"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi"},"partialJson":"{\"path\":\"/home/alex/.pi"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi/"},"partialJson":"{\"path\":\"/home/alex/.pi/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"jgYdn95cVVHnlK15jThXKFDi33TIiftGgJ3KsFCBxHXEsahrFLdD0FasGJUgNCkeOcxeIY69Uov0UFzm14w6bXzDtcojyk6Ag5VQhaFL5qlN7jIx+PoYas6+44wNoADbeprx/QgMXqpVM/npNx2Q2/TAy6+olAHJpjQzDDRUpoXUDTl4td0FQnENvJHPwBfopfdChLXxFLVrgba2DHQFS0bkhAcTwVRDdB6oKVta0Y137lB78cQNbLREd/NCSIdbsRIKJb4NMH80a3ZOt4W3ptSWrKjKBMLP0oz9jf+y/PYV1dpXNoJZDv+wz2dBRpBtGvurkUuGr8UWn6PVGJffthMHo4b+OhAPgCb777bzFlwoHUccvES4fKDQxVSrxKNU7ekg3FBZgfU95Bl/vv9xj9+R7aynkFZLVIGke09zJTut0mXYdolBaX7Hjkt2zcad2lPoSdgRz9QYEzlOM/8T5Mi1ynB0k4FTepTHagkWs+fbmvbNPnjIozJFxkuLt1BvCNnJOu6d+5NOIYJZoHLRUHlXJHaqrsaBDMfTWUX5XG0V3BT37SzT+XzPdcWYpf49+FOyedA70P3fluswT70Xo934zdQuRTa5tI+vgqZe9RroF2RTwoNKPsO6qwve9B+q9gzFoVERsQVyuqHBrZxkdSKRNSg0Lik/pNMltRuadVt5UrMG8SFv3ykj7VCv7qdGR3BBnHkQhLgWjnhVko1inlU2cWwYteoQef+2ZSi02dQy2ZqyPHyfNeduwADFf6J/+su25Dh8eeZNyFiJWmgEBBQIMJXmqmEl21XnFst8Np+P2Oh2x1XXye+Qj4k3xUYeCCYjIhHa1XaIsCIoJyOvbK48iLwmsDYObz77Gqy6HgDCDN9ShF9qHWIfmnzULaGtQjb6+0IxfRcBPOl7dA50fX1ln5ECqDrNxgX8beY86aHLhh2kGk6Su7oZ+cWbeJQ7PHnITmdysrk7x058m1WaueFqhIOEIkhKng5Fl1VonaoMPmf8OsiKTE05IN+w2OuMzt+mp7jWZ7R/CoWgD8hQrR2L46tYd00sm02w8VNBfLQx9B9Yajvyyq70jVfB6TWEcv1h83AEPqCNdgAMB2/MG02ph3Fqa8E/91cdwc7hmDPKJZ7oqwpMj/eBgt/QBRe5CrWa2yG/pSJmLPN15XHTof4VPS9UHJ3Bj3b07d8pDFEchD2Od3U7+wNm6ukloeKYk58G2HWnPNAUxIhqx8jYrbW9YcpD81VJVuQkHjGpe10r0A22UbeKY1DJXOXEywZVyuA4tW8aW7q58CD3MbBv0xk39UkBW7EKU8a+fPm5xKKy9js0b7Y5J/JhVIED27hUmfj4aoTOdn/tUXf5N8xnc3PcJru3BlK++II61nK0Dx10shS3IvCAJXtLJQVtdnGZj7G1pfs8pspxgC5Kru5wHG3jly2CNOWBQERJRnMTqFlyj8UxdTAIn2WEC0vXj4QHpYZZqQZpzYr3REQxy8uHghw1/FbUHy63z1Z31SOdzfUQUi/DPYir8juTagYq85ZV1/xGmGL6LL38+yiOqKs93pQuWLfD3juNk+Ml9rLsUD/vMit7iEDc2c2vH7Vrcb3r6bYiSX3J7oQXEJx0qq8TdanC97SnpPToEeQWmm5qnEsotzrJTnWA/Q6DD9ixi8Z0WLRrnvW+DA1lEhVho7z2Hu9ltZOEI/qhjVR2luuxVEZU4mOKEaF8EUKfdt8nK7puRx1/yM8fdso11/GFyrcwkj//REJ1+Avpct2bo8QPzu9z6hWHlQqp+1YEX/iHUaT/8ppYAkYIE0YYpefomD2j2SF4aetB7UAV17yMEKWt+NzGEEV5zrNMyGSWh/J3N0Bqd4LMPuQmgVA1MMBB07O0qr1/jKIBSXOLzE99DXEgyGVXALAXz4ED8WDBsZCG/P6hoPStrdcs1CJwMCs1hV676ZJxsY6H4jDS7reQPOZufr/MNN6RkZKgSf58B+d8EwEN8IZfIWgfT8wASEkSgAnYH2xAN5I8KDNGhPOwyQ/IZQTZklJHPjg45mJYk0JFABekgF7FPllVjQH8yPXiUhJEtxvxXGbamoR8JTxIj5efM7rmuwSJYf2P85hhcUGtSMAjOCkpKD48K6KXzcLGoOshw1hYozdg01fHInkMOzBJ+J85vlxF2n/wRDsxJZXta/3WkyrA7Wafj396idKmomX0FnaloEoapgNbCvBvbjtqGhxs50LfAQRNTuO1d9ZBx4KSklpwcJf/Y6xcKUeHgIDczuny2Hpq1svj6xyDhC+1MEKN6bs9kuko59T4Y1NBlrlC1h+qZt8IrHPl4L08rd+q/gurWCtbQ0WNvWQU8RWwXpf4rsax5+A9+mN3P1nje0TQybrWBGG46LvIrJW9xZBUrkAoJONdFliwXftVmLzjsziJfK3flEe8eXPRIwDZBiWmTLs/YJSFShiI7ReM/VS5FgQc1Y8x9OBFbyOeCAYFu8ynGabJnIbDKdmEjOaC5TFE+k1jkYgo7ZAHUt6zJtgwHaSDbmJAvqqqHE+8Kx7zAMvdxsUGNQdIFWUs74kdciX/Iz/gLHvM9LUVTkkXs6lrSF85rAtipkOiD7ZDcf4lkBS57PBXV/QmqbnjEq5O5QC6P+X+UDw7tsuc4ESwcYMOYIEUQCNEBnyQyWsz3pLP5UpfSwW3GS9vQtIqArXG9Nd9UJu+Yn4L/NuIBW58q+BTlZJ8yiUEvIv+oPqB3XWtyhZCrTFnpdptoe7yld/GL1+1YBgr2BLW4rJO8M5hfjL/f+0ZU1JbX+PR0dt0ojbLAOD7u0J/Grs/BPUiU4r18WuKJxbv+gF9wR5/TKWSxusJmDz2KE6TsFlDco389Buu3ThWSH/7rp6wbW2Tks6GJvSoL4a5aNIgw0Eryk0hcDxjtrbk0V+EtPifTJeZTRAd+A/xP55DOSirrivPOC4rE9sERVkhiEt5oX1wP7FKxiCUFd8blVgQDb2LJU99wJ7LuK7P85slNP3bg6vdvc5E+DGBlHc7Q3WrRKGbkbXsFYO8Exf+bd5FRvTPF/AKrkxrJYdnHmaXE7PeIdo/Y4hLbUvUMtqdMo1qQmsWWyeKPF2McNaW+Gatwoqdqrjm4jSV2kSnfBWYE/pqUsrSUFUTOd/lAQtqKMQRsiLgJEvuig7sEfGIelEopfS5l5rXJYd1GkUf3JQVi/CtRqtmTQ7iTyeUs1aNvcS79pSxXXwAkl7j2uFX/TdE+SLlWVRT16dHJ3z5L1Qkyi9hPFALahObY2bPfUsPHbgO40yn96YqW4GpVoSMdh34YTc8guvHSHcrob+JidHjMranUF4lks+aieHF8Cn6UfnDbHs/cHEkXi7lDfYV0xRaU4rfAjq5ULPe53J17pRQv5K7JKjKW3dRXpzA4wPY7Iyi4T28PgRD/BV3mRC5ct+3p+y7vd7f/kMFtfjZ/EFnqX0ENkr94fO6ra0LPfYIRduS8t1aE3+qmX1gG61rOPu+BE7h94Uhx5HNTOqkN610bOF85Yf+Ne2UIXyg+zopKy4044Jox6XT6bcGeLKqvcYWKYScVPZQThk9wHrhzq0KouOAZRtDdozvXY0mVEbKE2eH58/tmFfcuEUurGwYjkx0odhIQxKWP0x4D1PAQ4Wb3NEWw3JZBJU1UTgLvR7VRGJ+V0C0FnOTEGh8QnAEsJlSLOQUCxvU012njngqzkRk6GE2lc1qaktmRLF69nWt3VlPjjPQaDiod1ekIxfhb69+fK9gFK98g6Xb1OxU1TClx4JbEGj7YS/OqlisKeXlBuxaqpaOTao6IjtTiGQ7NLwHiOX3zrzx27DyLgO9\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi/"},"partialJson":"{\"path\":\"/home/alex/.pi/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"agent","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi/agent"},"partialJson":"{\"path\":\"/home/alex/.pi/agent"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi/agent"},"partialJson":"{\"path\":\"/home/alex/.pi/agent"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi/agent/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi/agent/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi/agent/skills"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi/agent/skills"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"\"}","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_end","contentIndex":1,"toolCall":{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|4KEqVc2slcGb5Kt2cKRtC1Sn03ipM0J0QqQwDxGBBXRn5P2enRS0FfEzPXfWk3w5vB6/9yquNv0pkIZyuw0YTO7fUhJj+07CDOG2lo09aqiQguObz2Hdf2VJnFPPC+Paejiu872KtPnrwIeJ7tnqfLKtTJ/XsxUI6/7Mo+HaPWmTvnC1JzR7ebHfgFTZbEX6z7naD1uHce5u0WitbsUJIxBDkJfvdEs2v/BkPT+6QofnL53/QLmi8kKxqTtRA54acW8xAgTg9COURBRObIWjkljt+x+Eeuz3ok1v3GMeqwP7TvQ4r8LnL71UzLEpxoWwBiEFWAsV4ev6wU/UTZU1Y1g1O7aEq9+HjfZ9Al0GRzdB7BLQJHUxF65EE7/dfABz0suu23hCf9QCeZWWQnSbMokQeg==","name":"ls","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"}},"partial":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940971044,"responseId":"GZurF7jGvQR4iZ6edf1mLtMzDqs1VY0Y82h6Gzwo9d//aeaK22Fru5sw5Cm6M8NsXm9JHnYaLKIK0HndVG4Wud6+O/AtXRmEbQbxls1pjfeC1xZB3Z5i9WtZxsu5ASQQ6KYyr+Vmv17DXe3EkZWyd8W5vIYJb3kx9JsosM7wJMBVZhNns+Vun+EByR2ai0jBTEXhynQNmy1xVJnVT7B0ocdigjsM7wNNIES4xujUSEHf/KtVhFiY017TWs+bao1BW7t5TBlkagxfGZlOl78/tJdmENuN0uApItP4FvR0kaIqnuTK1OUSzqppk5QCp9g/N09GvyKoczhZUBZ093aOHUDRsbhbH1WK+zi9YVp0MVNhjyug2DapvQwUlfhHz7SdTyVFy/H9Dt5kffxmtYoAyFE0XUpX"}}
{"type":"message_end","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":2667,"output":306,"cacheRead":0,"cacheWrite":0,"totalTokens":2973,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775940971044,"responseId":"FdvoSkW5eBfJcybVMtKWwCY29YCEuVzxB39GBrUzMMCRlFUY23DucrJzYKnJRlrO3P+JrAQSwcS3AzfQK/9Zd4gsaFVC61Tnz3EO88NEvJ/+H/nwWEzPwvn5gQ3MlE4/hWqB4uJKW3BnZna0RwMqu19/fnbmWo7jMEiSXA/dKM7VLE5mAMfp3LQCaaFQ4j9z5olYzFcW5Zv6HgOTsf452oF8NLqiOxFOrxbhmuElS7J5q5UU9WZb+TzWIbVvmvaeeeou0PypRojra02LnGRwmeahT1WY9RSQbmWQ/6FroozfZ2QzxWDv2wJR79jL7CnzIyLVTJaZEOAn/mK0ykSOhQ8zDcyQ7pRm2SER93gkdxekU9I7n9fmGd7Sf4ncMgkiw4uLbfY8ksivhklCGTi4jNOCioKb"}}
{"type":"tool_execution_start","toolCallId":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","toolName":"ls","args":{"path":"/home/alex/.pi/agent/skills/frontend"}}
{"type":"tool_execution_end","toolCallId":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","toolName":"ls","result":{"content":[{"type":"text","text":".pi/\nadapt/\nanimate/\narrange/\naudit/\nbolder/\nclarify/\ncolorize/\ncritique/\ndelight/\ndistill/\nextract/\nfrontend-design/\nharden/\nnormalize/\nonboard/\noptimize/\noverdrive/\npolish/\nquieter/\nteach-impeccable/\ntypeset/"}]},"isError":false}
{"type":"message_start","message":{"role":"toolResult","toolCallId":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","toolName":"ls","content":[{"type":"text","text":".pi/\nadapt/\nanimate/\narrange/\naudit/\nbolder/\nclarify/\ncolorize/\ncritique/\ndelight/\ndistill/\nextract/\nfrontend-design/\nharden/\nnormalize/\nonboard/\noptimize/\noverdrive/\npolish/\nquieter/\nteach-impeccable/\ntypeset/"}],"isError":false,"timestamp":1775940974426}}
{"type":"message_end","message":{"role":"toolResult","toolCallId":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","toolName":"ls","content":[{"type":"text","text":".pi/\nadapt/\nanimate/\narrange/\naudit/\nbolder/\nclarify/\ncolorize/\ncritique/\ndelight/\ndistill/\nextract/\nfrontend-design/\nharden/\nnormalize/\nonboard/\noptimize/\noverdrive/\npolish/\nquieter/\nteach-impeccable/\ntypeset/"}],"isError":false,"timestamp":1775940974426}}
{"type":"turn_end","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":2667,"output":306,"cacheRead":0,"cacheWrite":0,"totalTokens":2973,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775940971044,"responseId":"FdvoSkW5eBfJcybVMtKWwCY29YCEuVzxB39GBrUzMMCRlFUY23DucrJzYKnJRlrO3P+JrAQSwcS3AzfQK/9Zd4gsaFVC61Tnz3EO88NEvJ/+H/nwWEzPwvn5gQ3MlE4/hWqB4uJKW3BnZna0RwMqu19/fnbmWo7jMEiSXA/dKM7VLE5mAMfp3LQCaaFQ4j9z5olYzFcW5Zv6HgOTsf452oF8NLqiOxFOrxbhmuElS7J5q5UU9WZb+TzWIbVvmvaeeeou0PypRojra02LnGRwmeahT1WY9RSQbmWQ/6FroozfZ2QzxWDv2wJR79jL7CnzIyLVTJaZEOAn/mK0ykSOhQ8zDcyQ7pRm2SER93gkdxekU9I7n9fmGd7Sf4ncMgkiw4uLbfY8ksivhklCGTi4jNOCioKb"},"toolResults":[{"role":"toolResult","toolCallId":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","toolName":"ls","content":[{"type":"text","text":".pi/\nadapt/\nanimate/\narrange/\naudit/\nbolder/\nclarify/\ncolorize/\ncritique/\ndelight/\ndistill/\nextract/\nfrontend-design/\nharden/\nnormalize/\nonboard/\noptimize/\noverdrive/\npolish/\nquieter/\nteach-impeccable/\ntypeset/"}],"isError":false,"timestamp":1775940974426}]}
{"type":"turn_start"}
{"type":"message_start","message":{"role":"assistant","content":[],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_start","contentIndex":0,"partial":{"role":"assistant","content":[{"type":"thinking","thinking":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Evaluating skill loading**\n\nThe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" instructions","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" say","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" choose","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" existing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" but","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" include","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"c","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"c"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"c"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"av","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"eman","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" no","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" matter","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" what","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" So","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’ll","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" list","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" them","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" all","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"Skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" cav","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"eman","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" color","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"ize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" For","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" order","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’m","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" thinking","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"Order","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" cav","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"eman","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ->","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman ->"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman ->"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ->","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract ->"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract ->"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" color","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"ize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ->","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize ->"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize ->"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" makes","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" sense","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" but","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" maybe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" starting","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"audit","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" or","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" is","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" better","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"?","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better?"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better?"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" wonder","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" if","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"audit","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" or","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"color","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"ize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" should","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" come","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" first","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" when","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" figuring","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" out","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" token","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" drift","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Choosing frontend skills**\n\nI","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’m","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" considering","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" a","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" few","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" 1","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":")","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1)"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1)"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" cav","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"eman","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" (","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman ("}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman ("}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"brief","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" mode","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"),","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode),"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode),"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" 2","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":")","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2)"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2)"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" audit","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" identify","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" token","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" drift","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" 3","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":")","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3)"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3)"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" for","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" reusable","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" components","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" 4","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":")","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4)"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4)"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" align","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" system","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" 5","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":")","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5)"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5)"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" for","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" applying","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" tokens","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" CSS","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" variables","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" keep","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" it","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" simple","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" pick","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" just","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" a","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" small","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" set","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I'd","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" go","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" for","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" audit","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" But","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" since","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" include","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" cav","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"eman","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" should","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" list","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" it","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" 2","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"3","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" of","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Inspecting skill descriptions**\n\nI","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’m","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" looking","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" at","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" available","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"audit","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" want","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" start","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"c","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"c"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"c"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"av","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"eman","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" first","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" in","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" order","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" think","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" should","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" keep","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" this","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" concise","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" focusing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" on","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"audit","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" plus","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" either","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" or","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" But","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" which","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" one","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" should","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" pick","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"?","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick?"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick?"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" check","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skill","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" directories","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" review","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" descriptions","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" so","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’ll","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" read","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" SK","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SK"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SK"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"ILL","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".md","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" files","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" for","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" these","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" candidates","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" make","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" an","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" informed","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" choice","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Time","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" dig","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" into","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" details","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"!","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_end","contentIndex":0,"content":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_start","contentIndex":1,"partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{},"partialJson":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{},"partialJson":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"{\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"path","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{},"partialJson":"{\"path"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{},"partialJson":"{\"path"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"\":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{},"partialJson":"{\"path\":"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"HDJDumn7iz0UYNSlHvsqi1Ubed1OccKw3W3W4L3FRHXBJiLCYyQjesSHilh3HtnIK8ygtwLkv7RGbr5DVhCGA+jSbEdpq/3K7czhtZSFqkGCweB9XgAX+a6YGPL/a+ccTSXJNDHPBha9qxZIggxNxAw+vqSYNdAn4wKV5boLbLMqPZ8D9HFjaAHjk4ZJlf7VcMKlLxrRLhIflx4ciB4MnG7LxjTUqrgPCvp+bblScvDLua+NfJEEKLAi9pPAjFwQQhAiBJLJPtYUQXqGSuShryxJY8w7y0wTWl7oDDiJz2L1WaOK91yWIF6+ekrMxeytjVePMqL/VEDizSEMXDhJ4EXMVUvz2NNp8WYh+pKgQ+3hpk9GB9MBULkobhmzZx6wgWMF3bPHJ4isb8RGU18K2mUhEqUbrUVwe2seJYgjBcGiz/VG9FFtk9PQ7o4HYOOm42NDle5nG1Y1nsx41jDQLvWVFtgy2lu8FJhtW3k5Yf3atAUt/Fv7cHW1fOF9NeflG8RSWWqQjW+RkRFRlmvDgw0YWyPQH8gNl8VQKlBJe+3IKOqLC2uWiqXaIQ6gSNQFw1NgAHhZq7DGb+uhP39GKtZgrttTSH/VBADGXq40VM0x7IbPotlF4pPza8AtGfo2m5R3+fBeZcx29Kq2aVIXt8ucTHAgdFtl4lOEGEQYmCUjsbHIatTPF2UjtNembxMaWdsfnlh90nNi4r8ec4nNKj140+C6HSFE1hYfVXYnisgOtE/W/ed4F7N9A+nWofazGZpGPHLSjSiZkTcPQ/m/d1lgSQsJZdXKHlUPt78b73KdxuGUXIOWq+cf3MjsHqeX8TCbHNJQjNriwz3UN10Pu9IM+VHpkpbTVKmFQfhdCwCQTHn/VZMso3yblqpx08KW7Q2BZVm8FKX6mkTSXPFob0A7VvVI/z+KRulDbJ4iGmHO5eJShVie2Z/MRhZjyE94kTtzyIKQwDyP92kVKBzJaJwyBOX534UMGgGIq+LF8zMjn11eCckzfLtYmPUGggPuVDJtVeAkNIVCTaLthnxQ9bY62hjgnNgFkSR5QFqVdaQCE7eFlI7QVlS4GX5aFyK3rr9wtfcvaQDvjz10Gur4L95hV/O4B5tR8VGI01H2J8laaNfDalvXP/WztRspvHS0Z/3CNkd0v3W61eyuDd45REewnl0b4DxFZD4zVnqBizuOKggkHRhm2pvn6VB9prgJpFt52fvUBIjPk7UBkVB3tVqzcNSlmLE1+MnMaLqMZiCkQJKwrQ5MC6XM+x/jg+Aeb8Gr7rEILBgZV3sT3DsjKJpf4zFY/s6qZA7OG4/VWn+hkkIBE+jhTbDjS55JZcHD46LweqnOv3Sc5MwJ1yro3BynxrqUrn6wNeYEaQefrzCc2wu7rArLVu6Pfhkf9FBhTUN1Kwa69Lq10H/ZkAH++Fi6RlKUfmAxh9ZaSF17xO3tOaPQq8sg57zfiYr8bDrvn+H9VrJt8XmjfRCidU86YVJscYXWKjmswJqeRlMMoveDFlMq2RSvssMA3cslqO8s8pnEMtoFLeX1J6p1gO5LQhcm6pXS4rJP4R4z36uf8QXVhM4EN8WV4fUuKLtEtQ8/lNZmZZ1NJUxc1cDpm15tMAtmLLuv6GmcSeP5kQk5vhPeNvxeAmbSGxLNw6OaSHWLKXr03gJhEKkOwW1e/KD9yPNJaYe/8UVftjmSxXIrw1HRO6eedcJetNdmXiuwD1fy/fow9P1sy+k5hEYjXOWP9ClpGkmFyho/1Psb97mInsvr1TtRS2/pD1Uksr39JbLC1KBxJ4hI9h9evRF7/1mi4iIjMO2UIhMLYRBywg3vG4b4ruKWQUrkhs5kKHsbnBVrBYMF8TVC1lDoFgDDF+cIG+PY1/LZFKt9+ITigCpOTdJRby2AE9nxVWe4yGX7QLsjSfu6LgaV3wTz3Y2SaW8fK83/cjIxUuOzTgTWJL2h+5iB1fwKtL9m+B3CCCpBA4b/JTllfnd0MzSbA2Nwk8+ouRPBV8Zszl3GQ8OZFkHhgbgsJRPIBKo4CUor8SebPpAyScjCHTmEZZi/NZhnvp4jWMLivgvATB3wAEcRel+OwhwjyhdsDDP0BVgvPWqitEYV82TgszWgggIaKbXU3+AOPSlcL0pIm84wwuTzAN4zAD9kblDHIMkv6AQHlHoeDwi7xCvLpI0ft51oEhcu7DNC8iIv/utH0dJ/xpt2yjHsqdvUI14kI/zgmnDYx+q5JOAz197WCsUuK9NJybh1tMZVTWRfqb1N+WBeBFLCDhsZLYPLYXhGVCOfvMNjB9UA+rKceLcmYV2+vcXh5vKJ+tz4kKNQRT+cH05Wbegh6jz29C3GZdpLcqDqwjUllnXEq/c09yLqmoh5a1k6ti/1WIsYOtVbgFJNa3GfeOz5EqxFxq+e2SM8T7uqSIvuFRulxu4A/d1Mv7lfPabHfAj4AhIPKkVhptih2oCqyRIkeIKQy++KpiKAX+fKf8vFLsaNfP4cl9T3SxsmgpSpnACX15veXwvAJ2y8GElWlml3stuKn1f7iJIwTRpUBzMCFdMgBKUPXCSdt0TLNWHRYuPSKZvbmKSjvJn/fs5cLI9hLDm6SWC5m6eanWHOkszZ5FirpTXzF4OH3GIc6Y1Ub3VB0dMaA/Mn0RME+0lh+tt3hBLgscuABeQu5czEChYcTu35BfNS2GSI3P/cRe3katdNxWAQX9XcCIvnowSni+LjSeuTlmbv1RSICvPGgBXhWN76/AIWR4t6sPHNAcsCDcpAz8be35IRpXjpId0z0Fad2ft1Z2LJ1oSb3WdrR7VDec9DHrV9Yk3vbxJ+QSEK6XohOPnbBv+84yUeOjBKxkYpRWv47lpEmLmk1OuPEZD9jj/g/1jhxs6lVJOQoXrwI0kfS/QcrD+CRzPnsbxkC+Zd9gfCUg8IWxx16HhLVSbMNnrLTg8AsD0Xv06i+RHi4JSt+huanPqIHu/4IrcnurOZSR31H3Sa5bpGq83JAkmtdi/LqsqyeaX3fPbhrhvfHLyl7zfWNZCcRDn1J4pS004XailVtbwAGGmnWksVrmDGkxHVnPTaMtoz4P3oKXVLin8Z8NqdbB7l/Nxph6YpvdWzGeqgVTkFhTMqu15n2k0cQQPJUri+pjNSEhut5PTkEIJnE2BAURu1TaPEZcEvl1ZyoTiTSZKfsm3tsYtSZhQlLgelUlDcUcfR7isc0FgLrzsxeIF3HE6i9EouQ351HaAh9lPxsrP6rfhAb3rKw0wxrOJTxlFLxkvUNfHG7ZT2l5fE62kXtGbJnHzqczNSbA+oYqkQfQThX/R5LVqG8deFJufRmADmQ/oIjgVUBPYWaA5pJb5suClh/4VrPxFI/StlGjj9t05wopx5BXyRA7d31P4vXXWKdZ2xLyPus+fArsHDAJTy2f946VM0Owl61GHQ1ydvetO2OtxXbpyTXG2ixE+cjNP+o3aF+ID53Nqxc5mnfiMRguZ5rVe/aufJCeZm0LW7Il79p7kDVRyfiPfX/miHIpuKtXx7RAcf44h1ruT3WE60DyyhNU/Q1NX/CvOwgqlUoHsSd22f6bTtKGHrM+5gx7bhRnyCsC2m/e50ZJKQVmVuVxwQzIpf6ySYKjwEMs4SMHp0PJWLP79mGBu0Sgw8/ztyS/6OBRx91AWeCcDlevOL9ev2ngPdPhYkZGKgYNX+UOCRfO2tTSdEi9cUFEgb4tP3AmAO9LfqVM7Mi4R8c3gcJwBcqDMmJ8mQJI6P4EgbUmS8QnSoHoCK2VLlMrdSaOqQNz1vjfdtKNFFpHuFUFxjSfmuN1Pjv95vZaDoMvcJE0g+b/Zq/tAGnVE3+iiOEucMIo2xOi++t0Rx/qDUxDs0Njxcq4H55kiK4f28xlsRVJuhJQW4wT0Bq/Wyi/r7fOpcXw/Ox7QY0vCjSLv8d8gqOXHp4xP08RqDryEGtCT7roWzC9Ki1QhrX4E+kFYP9gGKCw1SCBHBl+a7bvug0xJh9q6eunoU521FilhJnmtRPJzvX4Ro/0uAt0zgB2W9DIdY/7pGFn6nm6vX+/Gk9841Mzu73+uStyJjSkvCHevbj3zkS6bJEFePBGw3JQ+VAOvlfAHtJ7rzykvn48zfrpTOSF4pV4uqFeOnm8HhWtvWA5W/Weyu4h9ujExtEj5ZTmm019bHronJ37dXgU4vsAKVagyiIYMTIlLjf5ausw2BBMh7wneN2pV2DHRqYMcZMazrwMElKKwzCoC6RMtYNArrlwrAndQQfpSo72wcT8NnfN9k6uYz3Bw9ETHu7Serm3dVwck60TcBoUW8D4XGY14afi4cAzcvmguZ9zF4SQPo8zpyePfBE8nTVB4yWqfP3rdbmZ4GnBBmFZ638wTWNMZRchMNozXfYKC49AWvu5cWRpdydEm94d3gm7ODqlsnfa4zVVLfYI+ULt4UpIxcso1dbgw6SFWfUunmTyMjVYY3EK1iDxHaSWR5NvYMq2ZoThRuq3YberYdGhn6ANH6S/mgz6q0Z00ws8yTPKzq+PZ4JEAFTfWlaecBBQ6Kpw0JBJJCP0YdQtvOOjMppo1dowARJZs5Ggu/QRqza//E/dAlDilhP7CnvXfcq9dqIs6SZv0GVCAusXm+zxeAU28Yporrj3C3XH4iLH1ybSwZYBK6OiTuWHEXVxmwLsTPAOIrUh7BZE7gV83zMVzlCLgKzzcYZNCT2Ixis6OBUb89LFGbrNrufHo5zajGJeLu8bkKgkjViTG4zTEbKhmBPpq4SMzsE0ypc9zrZKj+ZfP0a6d05besJmsslUuE431ayU05g7ZDHhE8cijoTVoFYVm9/f94lpD4H6g7KZuMxlGRSl5puDgrOKP7rT9792VkmhvwGugJedVJ0TL9LuNkzBLI2tOXirn089dkMBrZJ1dV4ZH5WXnV/wXmUoHjNtagz5kxodNm+OKX+L4O6lIVQppmOrfCQZgr93ILM/DvToKBNWfIeCN+ze98bf0HderNTA68o+F+Nv4Mb3TYbAbK5ZUyoOhBJ0BqH3x+IvJBEFjTj0RVKQYb3U4XyO/lp4La0Ca3CQ6YdOt0aXQM7mPBWDeFTXeERKRb1Vpo0nUhNI0csDVLPPbZu3eW0ElDIVPcyYqZmqMieFK7qEx8yIn+bP/e4chkc13wSbE2QAJUi5+apZtSd2Rf2xiRPkCVqIOJogACoPuGae6ZMN+yWbAKls1vepjBhzAKc4LpWOh/N12FSY7jG9BDt5F65smfJUyw/xUvyb9F5sCEpKoFvYK5BbnD0U1u0HvRgTk/HvfRfmYGMxoB920DPQkqrGhlWf3yu6o5I6+1scjAaJoIOfwwAM2HYiK640p9dBTrgupn6d4LWeSdvH4JM2egFZvtkQJig19x6HbXC+JrYgw2/Uuloo4x2OfSK3G5OQcNZshLY5+NhM3kHkln99A2Fb83dRMuHDUnhJMGEnFHjA3NboYzkbCK2XvfREvRDVu2Y7NWAXgZDnaVGGY5s9PwUStr2Rr1VDj5tmQR1mEd06S1C+iQSecacRFaoExFfc0UKY0t/Kh044ZkDzBagR/5nHRWUScHcKE6zAF3PENOy9UdJxmLbS12dBgC0sHmukUIeWEDDQ7rPCKxg3E5njVe0+gu0nPmC8GaRKTe/e2IdbPfwurYD++MeuRu9yZouE6HhV+qA0aE6N3QY6cMacQbmihb1fxpP1D8qJmwvuiUMoVCKo02XFT7/yPkJPuw790w0JxT2BSTAI67IaUgtOsPqEuk5ZXwByA2kr58bv28h+reGP8SC0RWHm9BPPDkw4lGmz2GLRB8UvLLm4z5S8zChcNVl9dRzkSokpzOZZNfhJ3hb7/MgttVFbZFxoq7RRBoEkaG55y4jqcpfNiiuL/dyhGLjvUx8OZBrlaTMqsLMJVQOyPybyPAGtBnlJAstlwSgNMNlCDVLP5zTMgBWrckJhhTpZxBG5Xpqbgpbztx+gHo69mYb3IpNaYRWpWJp0hdxIKD+gOrOYe8KmjjtDySA/3BnQLqNHd3WBmTCLoZ8Qx0/zlg6hLxYlx7RFMAlu+YaZSDiRdYYllSekSezJQp4Ym9TLLxvCo5UpdqXwq3zNhpDRMTxWhTxDQUMD1JeMX68M++7L+YsewLf0h/3g+VzfJE/Pqxi0XwmhEIvBaKbM01lJB2BaUneusSjMsfCzRflJ7F2gmygT8oxNAPGvQR5RZJEQfkvv06TMAPGIjeGJDdITP7cEgYfEvbeE0ua/xYjxfISqG3yTehdonjrrEXxhH0YXt2aUq5Onn7esksD4apYCNecZ4zTrfDJTioiqNB8mqg7iivVf1lCPiGkpKOnvi+xV4np/w3B+Au5plViPRwqXplykyXUYAcRIzCsRKZoXhBPy3mldvDzD+f3Jxx+jwfrUVoouI4JTGAaZL8YfLZnuEl6ZP5S4U+DEr+93MI0CfcKuFaRzyjzqSJ2frudl2tKxqujT/XIgItmy4cmEwqZzVkPc4xG8zzHmfNt0o/1waPTtw8sW4oRsiIUL1oUQLZqjifh+Ln2+XkxxgsOMItTomc3H2xKF42PmTCwXG1zfkL/03T+PPVw2Nxe+a95lm997GzBqdMhd04cfPR/SCRT+Z+chy42l9v8Ag3Dbw//u53K4GqXNJkvFc2T93mBkJd4xRFYIYpDLW30Xy5ijIdFBbnB9d1QxHGtZ7fBfjv4xERh+7DpdCKM59vsNGV3NGFgKsilI/Mj1PdRFaXE2eKp73HwR8DnoXoY9aiglKXT630nY10CLFlQKwxomIMx5KoSO3GOMjpkHzxYZUK9x8R+JCwpCoTImMmIKuyVZPU/ML2pz8YepQPxwdPCp25R9pHSCcunbGX9bBSdnUoUXwivqWdcXsbOh3f3/7Lv4Zk7p3E1CLFneNFg/Oia4QHIkwgcQIt+M5TM0dHamEeGM7gX8SoUAmQIz/9+0DdD1iqzrkM80jbOtVQFEiFUDotkJGrMmylYSr5EeyRX1WLggJpkJdZfJ5TMIzVK2qnz0BoIE8FVoSYyRBx4kkVbQ2BGQtM80iUpDUk9/YTwJ9fjRzeql+fqdzitEuqXE/igIO6xbaO3RTxc9Jqe/LXvNJHFaeF+/k7QsuNxcT1ZW2r/udCotuF2wIkGtHLcM6wpvTA5FbH4iQF9069sfJ0OSlccMSjMq6QRwQDj3TPvFyCskbkYDycr9bMU4FSx/yHYGvAhBX4jO2J1qVzuMLOuvrzWaYOBUleLHp/A/LCPpCa7wjYkF+emOtJhieuv0NihCaiGj2xN87Hsb0GP13kJ/wFEAwERvAkuE86DPnG8KwiAhHCRLmbYc9YUPcg6ZM6AyOy74cbqQs/DAy43s9XfZxD5yB9ukGXYxfuFhNYI37ZD5RglSNqC7ygCnQPJMEVlStUZkwMwFtYKlRk/7EayrXAVkdT3/7VRw9eIbWt8gnYkguBpFXvubAQzpBdaZeh2pmmP7UzlropvcnbyjQxB1kRz6nXo0g7dmpoUdzCtpQ8NM7UBDOd4U0okVcdD1v+Be5wDD4ivwlMVgjjHKjsHRneyxUEZwFbwj64kBblHRcLJ3FEoT0hEHFsP0VlQelQxN5C75DWaxBCCtbQ9nJ0VSWHjpXb5IU8f9opCJoEmahV6XWxJmCnMAub7958DIO1GyNuiaZ669xkG4a5OKgzxgcibJV20iR8muc7g2KHkDhlX2XCvpIpH4N1CzqGeu5CdRbfJJVm5mm+j9R3hi2cQtgD80D+UmixblWdDw38WF4utPUaCYIQW4YZAr++UBEZj5MESYipRr25dM7B1ePapTGRBV4dCvUy/qXbfhAI98WIyrDm8YEJd633dHD7dkeIA2F30cU2ABO6Pz5Mlejo7qS9NmJDq2ORVbJIkTl1qxaUtbdTU8Xsg8y3iH21IcKR37R2ZLugBnJjA2dDszGOlwxc9ad7anzViHGSmhlHOqTb4jyf41BZaaLzUIjoOiQowfyLg/JSZZ/wril0GFOG4FhUxKikKmz3OXXtiUCNH5TC6GbOJJKDcBCwf3Ksk3QU5mTHxaCTBIpWvfWpcm7cbVcFLX1XuQ6xDDC32pot4oiV6DMzMiKcJJxlrvh3iDwPvi31ekUxrARlaBshgtwPkt4wXr6N6EDexjDCwNG7KQVWVVUilSxUh2Zo2MO+zgKVLoZJB0kfOb2PZnrW9ktvTSVpwANCnjCTaUnSZ20+tSvTi7Bqx2AuPYOc437JBYf8QYr9yrWHGdQXYyUFC6GrRlwgHKvE2Vb4uDVMX6xv+PoV1aWiXUn/6V4+RfOBvWSt22hQIpug5+rXQKVqisjui6mW7BdD/U/YUqhGC5gSAmCGlgSQ2EXGT+VXH22/pDYEpjPZN2KtwPioPpECO8R59Pr+YenL+eUJmnaLSahuWW6dCMN8O+s9yubPSFZvf0+8tmupBwqQr1zXfR8LhEYNgw3rsFHy304Z4ujL24DpvVP9D8JgAom6mzjzKVJsJMREfJsncE4wJdMTiKCVnidaRZXvuirTABFtl5ZDVRrFYpSdXOLE3z2vcDTTk5EN54eLghI3RcaE9QQDAI47DAOxzGrcRn8v8jRxFP0yzGJPDPv4EPXc9z9E8Np808H5kF8wEWYnJH6R1CLpvgwL3ZfztRvcEdWqySUMPEQDU7QbFOdqa9dqKF84SazF1u/QsV2xrb3QlDSsJF8KotDgg5Yi1ktIpFSK4qYxcdg0f/5MOdL65yjY8Ppt8XpqNpM9m7B5tKRAZ2cfxRfOXOTjQrs3YCfF8SehnPcJrVlrlE5yDXEUim2MC8OuG6sr6aKwYJQw3eUJElwVv39ZgzNglLN6/YqSxSGSRGI6xXHI875AyWKUkcWMEsQub5s+4MaB9RR0O3dRRRimxWTrx9bbF/Xmi3bsc1edFweMUoDRXfii6nBl5vrxzgEnMOqFRxlvm3bZiuXVD3ZKjKbAoK/VsZuMEOyHnEKJx8jLZ6q3p2xWWLEqXz24fqw4r/LmfYf0YzEN48FAb3c4D0lIjelreQBAX6FgLhMf/r/p4T4ReLcC4vowfuc8XdvxG155MHkppeDmucaRKLStqUzD1g0Th7LQfe6Z79SqfDG77XX4pvYyUT8KwcFILb4sPyGL+Um6vuU/O2yGviXCBo9r9b00OJpjJznTjrGDTxZqI+1tEXQYRnzb0yMjtunfI2a37GvBs3G5njb4V8pisgvLuaIdeB0DScr3hsunU8K4tZuwsybHcP8KDpQ=\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{},"partialJson":"{\"path\":"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"\"/","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"HDJDumn7iz0UYNSlHvsqi1Ubed1OccKw3W3W4L3FRHXBJiLCYyQjesSHilh3HtnIK8ygtwLkv7RGbr5DVhCGA+jSbEdpq/3K7czhtZSFqkGCweB9XgAX+a6YGPL/a+ccTSXJNDHPBha9qxZIggxNxAw+vqSYNdAn4wKV5boLbLMqPZ8D9HFjaAHjk4ZJlf7VcMKlLxrRLhIflx4ciB4MnG7LxjTUqrgPCvp+bblScvDLua+NfJEEKLAi9pPAjFwQQhAiBJLJPtYUQXqGSuShryxJY8w7y0wTWl7oDDiJz2L1WaOK91yWIF6+ekrMxeytjVePMqL/VEDizSEMXDhJ4EXMVUvz2NNp8WYh+pKgQ+3hpk9GB9MBULkobhmzZx6wgWMF3bPHJ4isb8RGU18K2mUhEqUbrUVwe2seJYgjBcGiz/VG9FFtk9PQ7o4HYOOm42NDle5nG1Y1nsx41jDQLvWVFtgy2lu8FJhtW3k5Yf3atAUt/Fv7cHW1fOF9NeflG8RSWWqQjW+RkRFRlmvDgw0YWyPQH8gNl8VQKlBJe+3IKOqLC2uWiqXaIQ6gSNQFw1NgAHhZq7DGb+uhP39GKtZgrttTSH/VBADGXq40VM0x7IbPotlF4pPza8AtGfo2m5R3+fBeZcx29Kq2aVIXt8ucTHAgdFtl4lOEGEQYmCUjsbHIatTPF2UjtNembxMaWdsfnlh90nNi4r8ec4nNKj140+C6HSFE1hYfVXYnisgOtE/W/ed4F7N9A+nWofazGZpGPHLSjSiZkTcPQ/m/d1lgSQsJZdXKHlUPt78b73KdxuGUXIOWq+cf3MjsHqeX8TCbHNJQjNriwz3UN10Pu9IM+VHpkpbTVKmFQfhdCwCQTHn/VZMso3yblqpx08KW7Q2BZVm8FKX6mkTSXPFob0A7VvVI/z+KRulDbJ4iGmHO5eJShVie2Z/MRhZjyE94kTtzyIKQwDyP92kVKBzJaJwyBOX534UMGgGIq+LF8zMjn11eCckzfLtYmPUGggPuVDJtVeAkNIVCTaLthnxQ9bY62hjgnNgFkSR5QFqVdaQCE7eFlI7QVlS4GX5aFyK3rr9wtfcvaQDvjz10Gur4L95hV/O4B5tR8VGI01H2J8laaNfDalvXP/WztRspvHS0Z/3CNkd0v3W61eyuDd45REewnl0b4DxFZD4zVnqBizuOKggkHRhm2pvn6VB9prgJpFt52fvUBIjPk7UBkVB3tVqzcNSlmLE1+MnMaLqMZiCkQJKwrQ5MC6XM+x/jg+Aeb8Gr7rEILBgZV3sT3DsjKJpf4zFY/s6qZA7OG4/VWn+hkkIBE+jhTbDjS55JZcHD46LweqnOv3Sc5MwJ1yro3BynxrqUrn6wNeYEaQefrzCc2wu7rArLVu6Pfhkf9FBhTUN1Kwa69Lq10H/ZkAH++Fi6RlKUfmAxh9ZaSF17xO3tOaPQq8sg57zfiYr8bDrvn+H9VrJt8XmjfRCidU86YVJscYXWKjmswJqeRlMMoveDFlMq2RSvssMA3cslqO8s8pnEMtoFLeX1J6p1gO5LQhcm6pXS4rJP4R4z36uf8QXVhM4EN8WV4fUuKLtEtQ8/lNZmZZ1NJUxc1cDpm15tMAtmLLuv6GmcSeP5kQk5vhPeNvxeAmbSGxLNw6OaSHWLKXr03gJhEKkOwW1e/KD9yPNJaYe/8UVftjmSxXIrw1HRO6eedcJetNdmXiuwD1fy/fow9P1sy+k5hEYjXOWP9ClpGkmFyho/1Psb97mInsvr1TtRS2/pD1Uksr39JbLC1KBxJ4hI9h9evRF7/1mi4iIjMO2UIhMLYRBywg3vG4b4ruKWQUrkhs5kKHsbnBVrBYMF8TVC1lDoFgDDF+cIG+PY1/LZFKt9+ITigCpOTdJRby2AE9nxVWe4yGX7QLsjSfu6LgaV3wTz3Y2SaW8fK83/cjIxUuOzTgTWJL2h+5iB1fwKtL9m+B3CCCpBA4b/JTllfnd0MzSbA2Nwk8+ouRPBV8Zszl3GQ8OZFkHhgbgsJRPIBKo4CUor8SebPpAyScjCHTmEZZi/NZhnvp4jWMLivgvATB3wAEcRel+OwhwjyhdsDDP0BVgvPWqitEYV82TgszWgggIaKbXU3+AOPSlcL0pIm84wwuTzAN4zAD9kblDHIMkv6AQHlHoeDwi7xCvLpI0ft51oEhcu7DNC8iIv/utH0dJ/xpt2yjHsqdvUI14kI/zgmnDYx+q5JOAz197WCsUuK9NJybh1tMZVTWRfqb1N+WBeBFLCDhsZLYPLYXhGVCOfvMNjB9UA+rKceLcmYV2+vcXh5vKJ+tz4kKNQRT+cH05Wbegh6jz29C3GZdpLcqDqwjUllnXEq/c09yLqmoh5a1k6ti/1WIsYOtVbgFJNa3GfeOz5EqxFxq+e2SM8T7uqSIvuFRulxu4A/d1Mv7lfPabHfAj4AhIPKkVhptih2oCqyRIkeIKQy++KpiKAX+fKf8vFLsaNfP4cl9T3SxsmgpSpnACX15veXwvAJ2y8GElWlml3stuKn1f7iJIwTRpUBzMCFdMgBKUPXCSdt0TLNWHRYuPSKZvbmKSjvJn/fs5cLI9hLDm6SWC5m6eanWHOkszZ5FirpTXzF4OH3GIc6Y1Ub3VB0dMaA/Mn0RME+0lh+tt3hBLgscuABeQu5czEChYcTu35BfNS2GSI3P/cRe3katdNxWAQX9XcCIvnowSni+LjSeuTlmbv1RSICvPGgBXhWN76/AIWR4t6sPHNAcsCDcpAz8be35IRpXjpId0z0Fad2ft1Z2LJ1oSb3WdrR7VDec9DHrV9Yk3vbxJ+QSEK6XohOPnbBv+84yUeOjBKxkYpRWv47lpEmLmk1OuPEZD9jj/g/1jhxs6lVJOQoXrwI0kfS/QcrD+CRzPnsbxkC+Zd9gfCUg8IWxx16HhLVSbMNnrLTg8AsD0Xv06i+RHi4JSt+huanPqIHu/4IrcnurOZSR31H3Sa5bpGq83JAkmtdi/LqsqyeaX3fPbhrhvfHLyl7zfWNZCcRDn1J4pS004XailVtbwAGGmnWksVrmDGkxHVnPTaMtoz4P3oKXVLin8Z8NqdbB7l/Nxph6YpvdWzGeqgVTkFhTMqu15n2k0cQQPJUri+pjNSEhut5PTkEIJnE2BAURu1TaPEZcEvl1ZyoTiTSZKfsm3tsYtSZhQlLgelUlDcUcfR7isc0FgLrzsxeIF3HE6i9EouQ351HaAh9lPxsrP6rfhAb3rKw0wxrOJTxlFLxkvUNfHG7ZT2l5fE62kXtGbJnHzqczNSbA+oYqkQfQThX/R5LVqG8deFJufRmADmQ/oIjgVUBPYWaA5pJb5suClh/4VrPxFI/StlGjj9t05wopx5BXyRA7d31P4vXXWKdZ2xLyPus+fArsHDAJTy2f946VM0Owl61GHQ1ydvetO2OtxXbpyTXG2ixE+cjNP+o3aF+ID53Nqxc5mnfiMRguZ5rVe/aufJCeZm0LW7Il79p7kDVRyfiPfX/miHIpuKtXx7RAcf44h1ruT3WE60DyyhNU/Q1NX/CvOwgqlUoHsSd22f6bTtKGHrM+5gx7bhRnyCsC2m/e50ZJKQVmVuVxwQzIpf6ySYKjwEMs4SMHp0PJWLP79mGBu0Sgw8/ztyS/6OBRx91AWeCcDlevOL9ev2ngPdPhYkZGKgYNX+UOCRfO2tTSdEi9cUFEgb4tP3AmAO9LfqVM7Mi4R8c3gcJwBcqDMmJ8mQJI6P4EgbUmS8QnSoHoCK2VLlMrdSaOqQNz1vjfdtKNFFpHuFUFxjSfmuN1Pjv95vZaDoMvcJE0g+b/Zq/tAGnVE3+iiOEucMIo2xOi++t0Rx/qDUxDs0Njxcq4H55kiK4f28xlsRVJuhJQW4wT0Bq/Wyi/r7fOpcXw/Ox7QY0vCjSLv8d8gqOXHp4xP08RqDryEGtCT7roWzC9Ki1QhrX4E+kFYP9gGKCw1SCBHBl+a7bvug0xJh9q6eunoU521FilhJnmtRPJzvX4Ro/0uAt0zgB2W9DIdY/7pGFn6nm6vX+/Gk9841Mzu73+uStyJjSkvCHevbj3zkS6bJEFePBGw3JQ+VAOvlfAHtJ7rzykvn48zfrpTOSF4pV4uqFeOnm8HhWtvWA5W/Weyu4h9ujExtEj5ZTmm019bHronJ37dXgU4vsAKVagyiIYMTIlLjf5ausw2BBMh7wneN2pV2DHRqYMcZMazrwMElKKwzCoC6RMtYNArrlwrAndQQfpSo72wcT8NnfN9k6uYz3Bw9ETHu7Serm3dVwck60TcBoUW8D4XGY14afi4cAzcvmguZ9zF4SQPo8zpyePfBE8nTVB4yWqfP3rdbmZ4GnBBmFZ638wTWNMZRchMNozXfYKC49AWvu5cWRpdydEm94d3gm7ODqlsnfa4zVVLfYI+ULt4UpIxcso1dbgw6SFWfUunmTyMjVYY3EK1iDxHaSWR5NvYMq2ZoThRuq3YberYdGhn6ANH6S/mgz6q0Z00ws8yTPKzq+PZ4JEAFTfWlaecBBQ6Kpw0JBJJCP0YdQtvOOjMppo1dowARJZs5Ggu/QRqza//E/dAlDilhP7CnvXfcq9dqIs6SZv0GVCAusXm+zxeAU28Yporrj3C3XH4iLH1ybSwZYBK6OiTuWHEXVxmwLsTPAOIrUh7BZE7gV83zMVzlCLgKzzcYZNCT2Ixis6OBUb89LFGbrNrufHo5zajGJeLu8bkKgkjViTG4zTEbKhmBPpq4SMzsE0ypc9zrZKj+ZfP0a6d05besJmsslUuE431ayU05g7ZDHhE8cijoTVoFYVm9/f94lpD4H6g7KZuMxlGRSl5puDgrOKP7rT9792VkmhvwGugJedVJ0TL9LuNkzBLI2tOXirn089dkMBrZJ1dV4ZH5WXnV/wXmUoHjNtagz5kxodNm+OKX+L4O6lIVQppmOrfCQZgr93ILM/DvToKBNWfIeCN+ze98bf0HderNTA68o+F+Nv4Mb3TYbAbK5ZUyoOhBJ0BqH3x+IvJBEFjTj0RVKQYb3U4XyO/lp4La0Ca3CQ6YdOt0aXQM7mPBWDeFTXeERKRb1Vpo0nUhNI0csDVLPPbZu3eW0ElDIVPcyYqZmqMieFK7qEx8yIn+bP/e4chkc13wSbE2QAJUi5+apZtSd2Rf2xiRPkCVqIOJogACoPuGae6ZMN+yWbAKls1vepjBhzAKc4LpWOh/N12FSY7jG9BDt5F65smfJUyw/xUvyb9F5sCEpKoFvYK5BbnD0U1u0HvRgTk/HvfRfmYGMxoB920DPQkqrGhlWf3yu6o5I6+1scjAaJoIOfwwAM2HYiK640p9dBTrgupn6d4LWeSdvH4JM2egFZvtkQJig19x6HbXC+JrYgw2/Uuloo4x2OfSK3G5OQcNZshLY5+NhM3kHkln99A2Fb83dRMuHDUnhJMGEnFHjA3NboYzkbCK2XvfREvRDVu2Y7NWAXgZDnaVGGY5s9PwUStr2Rr1VDj5tmQR1mEd06S1C+iQSecacRFaoExFfc0UKY0t/Kh044ZkDzBagR/5nHRWUScHcKE6zAF3PENOy9UdJxmLbS12dBgC0sHmukUIeWEDDQ7rPCKxg3E5njVe0+gu0nPmC8GaRKTe/e2IdbPfwurYD++MeuRu9yZouE6HhV+qA0aE6N3QY6cMacQbmihb1fxpP1D8qJmwvuiUMoVCKo02XFT7/yPkJPuw790w0JxT2BSTAI67IaUgtOsPqEuk5ZXwByA2kr58bv28h+reGP8SC0RWHm9BPPDkw4lGmz2GLRB8UvLLm4z5S8zChcNVl9dRzkSokpzOZZNfhJ3hb7/MgttVFbZFxoq7RRBoEkaG55y4jqcpfNiiuL/dyhGLjvUx8OZBrlaTMqsLMJVQOyPybyPAGtBnlJAstlwSgNMNlCDVLP5zTMgBWrckJhhTpZxBG5Xpqbgpbztx+gHo69mYb3IpNaYRWpWJp0hdxIKD+gOrOYe8KmjjtDySA/3BnQLqNHd3WBmTCLoZ8Qx0/zlg6hLxYlx7RFMAlu+YaZSDiRdYYllSekSezJQp4Ym9TLLxvCo5UpdqXwq3zNhpDRMTxWhTxDQUMD1JeMX68M++7L+YsewLf0h/3g+VzfJE/Pqxi0XwmhEIvBaKbM01lJB2BaUneusSjMsfCzRflJ7F2gmygT8oxNAPGvQR5RZJEQfkvv06TMAPGIjeGJDdITP7cEgYfEvbeE0ua/xYjxfISqG3yTehdonjrrEXxhH0YXt2aUq5Onn7esksD4apYCNecZ4zTrfDJTioiqNB8mqg7iivVf1lCPiGkpKOnvi+xV4np/w3B+Au5plViPRwqXplykyXUYAcRIzCsRKZoXhBPy3mldvDzD+f3Jxx+jwfrUVoouI4JTGAaZL8YfLZnuEl6ZP5S4U+DEr+93MI0CfcKuFaRzyjzqSJ2frudl2tKxqujT/XIgItmy4cmEwqZzVkPc4xG8zzHmfNt0o/1waPTtw8sW4oRsiIUL1oUQLZqjifh+Ln2+XkxxgsOMItTomc3H2xKF42PmTCwXG1zfkL/03T+PPVw2Nxe+a95lm997GzBqdMhd04cfPR/SCRT+Z+chy42l9v8Ag3Dbw//u53K4GqXNJkvFc2T93mBkJd4xRFYIYpDLW30Xy5ijIdFBbnB9d1QxHGtZ7fBfjv4xERh+7DpdCKM59vsNGV3NGFgKsilI/Mj1PdRFaXE2eKp73HwR8DnoXoY9aiglKXT630nY10CLFlQKwxomIMx5KoSO3GOMjpkHzxYZUK9x8R+JCwpCoTImMmIKuyVZPU/ML2pz8YepQPxwdPCp25R9pHSCcunbGX9bBSdnUoUXwivqWdcXsbOh3f3/7Lv4Zk7p3E1CLFneNFg/Oia4QHIkwgcQIt+M5TM0dHamEeGM7gX8SoUAmQIz/9+0DdD1iqzrkM80jbOtVQFEiFUDotkJGrMmylYSr5EeyRX1WLggJpkJdZfJ5TMIzVK2qnz0BoIE8FVoSYyRBx4kkVbQ2BGQtM80iUpDUk9/YTwJ9fjRzeql+fqdzitEuqXE/igIO6xbaO3RTxc9Jqe/LXvNJHFaeF+/k7QsuNxcT1ZW2r/udCotuF2wIkGtHLcM6wpvTA5FbH4iQF9069sfJ0OSlccMSjMq6QRwQDj3TPvFyCskbkYDycr9bMU4FSx/yHYGvAhBX4jO2J1qVzuMLOuvrzWaYOBUleLHp/A/LCPpCa7wjYkF+emOtJhieuv0NihCaiGj2xN87Hsb0GP13kJ/wFEAwERvAkuE86DPnG8KwiAhHCRLmbYc9YUPcg6ZM6AyOy74cbqQs/DAy43s9XfZxD5yB9ukGXYxfuFhNYI37ZD5RglSNqC7ygCnQPJMEVlStUZkwMwFtYKlRk/7EayrXAVkdT3/7VRw9eIbWt8gnYkguBpFXvubAQzpBdaZeh2pmmP7UzlropvcnbyjQxB1kRz6nXo0g7dmpoUdzCtpQ8NM7UBDOd4U0okVcdD1v+Be5wDD4ivwlMVgjjHKjsHRneyxUEZwFbwj64kBblHRcLJ3FEoT0hEHFsP0VlQelQxN5C75DWaxBCCtbQ9nJ0VSWHjpXb5IU8f9opCJoEmahV6XWxJmCnMAub7958DIO1GyNuiaZ669xkG4a5OKgzxgcibJV20iR8muc7g2KHkDhlX2XCvpIpH4N1CzqGeu5CdRbfJJVm5mm+j9R3hi2cQtgD80D+UmixblWdDw38WF4utPUaCYIQW4YZAr++UBEZj5MESYipRr25dM7B1ePapTGRBV4dCvUy/qXbfhAI98WIyrDm8YEJd633dHD7dkeIA2F30cU2ABO6Pz5Mlejo7qS9NmJDq2ORVbJIkTl1qxaUtbdTU8Xsg8y3iH21IcKR37R2ZLugBnJjA2dDszGOlwxc9ad7anzViHGSmhlHOqTb4jyf41BZaaLzUIjoOiQowfyLg/JSZZ/wril0GFOG4FhUxKikKmz3OXXtiUCNH5TC6GbOJJKDcBCwf3Ksk3QU5mTHxaCTBIpWvfWpcm7cbVcFLX1XuQ6xDDC32pot4oiV6DMzMiKcJJxlrvh3iDwPvi31ekUxrARlaBshgtwPkt4wXr6N6EDexjDCwNG7KQVWVVUilSxUh2Zo2MO+zgKVLoZJB0kfOb2PZnrW9ktvTSVpwANCnjCTaUnSZ20+tSvTi7Bqx2AuPYOc437JBYf8QYr9yrWHGdQXYyUFC6GrRlwgHKvE2Vb4uDVMX6xv+PoV1aWiXUn/6V4+RfOBvWSt22hQIpug5+rXQKVqisjui6mW7BdD/U/YUqhGC5gSAmCGlgSQ2EXGT+VXH22/pDYEpjPZN2KtwPioPpECO8R59Pr+YenL+eUJmnaLSahuWW6dCMN8O+s9yubPSFZvf0+8tmupBwqQr1zXfR8LhEYNgw3rsFHy304Z4ujL24DpvVP9D8JgAom6mzjzKVJsJMREfJsncE4wJdMTiKCVnidaRZXvuirTABFtl5ZDVRrFYpSdXOLE3z2vcDTTk5EN54eLghI3RcaE9QQDAI47DAOxzGrcRn8v8jRxFP0yzGJPDPv4EPXc9z9E8Np808H5kF8wEWYnJH6R1CLpvgwL3ZfztRvcEdWqySUMPEQDU7QbFOdqa9dqKF84SazF1u/QsV2xrb3QlDSsJF8KotDgg5Yi1ktIpFSK4qYxcdg0f/5MOdL65yjY8Ppt8XpqNpM9m7B5tKRAZ2cfxRfOXOTjQrs3YCfF8SehnPcJrVlrlE5yDXEUim2MC8OuG6sr6aKwYJQw3eUJElwVv39ZgzNglLN6/YqSxSGSRGI6xXHI875AyWKUkcWMEsQub5s+4MaB9RR0O3dRRRimxWTrx9bbF/Xmi3bsc1edFweMUoDRXfii6nBl5vrxzgEnMOqFRxlvm3bZiuXVD3ZKjKbAoK/VsZuMEOyHnEKJx8jLZ6q3p2xWWLEqXz24fqw4r/LmfYf0YzEN48FAb3c4D0lIjelreQBAX6FgLhMf/r/p4T4ReLcC4vowfuc8XdvxG155MHkppeDmucaRKLStqUzD1g0Th7LQfe6Z79SqfDG77XX4pvYyUT8KwcFILb4sPyGL+Um6vuU/O2yGviXCBo9r9b00OJpjJznTjrGDTxZqI+1tEXQYRnzb0yMjtunfI2a37GvBs3G5njb4V8pisgvLuaIdeB0DScr3hsunU8K4tZuwsybHcP8KDpQ=\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/"},"partialJson":"{\"path\":\"/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"HDJDumn7iz0UYNSlHvsqi1Ubed1OccKw3W3W4L3FRHXBJiLCYyQjesSHilh3HtnIK8ygtwLkv7RGbr5DVhCGA+jSbEdpq/3K7czhtZSFqkGCweB9XgAX+a6YGPL/a+ccTSXJNDHPBha9qxZIggxNxAw+vqSYNdAn4wKV5boLbLMqPZ8D9HFjaAHjk4ZJlf7VcMKlLxrRLhIflx4ciB4MnG7LxjTUqrgPCvp+bblScvDLua+NfJEEKLAi9pPAjFwQQhAiBJLJPtYUQXqGSuShryxJY8w7y0wTWl7oDDiJz2L1WaOK91yWIF6+ekrMxeytjVePMqL/VEDizSEMXDhJ4EXMVUvz2NNp8WYh+pKgQ+3hpk9GB9MBULkobhmzZx6wgWMF3bPHJ4isb8RGU18K2mUhEqUbrUVwe2seJYgjBcGiz/VG9FFtk9PQ7o4HYOOm42NDle5nG1Y1nsx41jDQLvWVFtgy2lu8FJhtW3k5Yf3atAUt/Fv7cHW1fOF9NeflG8RSWWqQjW+RkRFRlmvDgw0YWyPQH8gNl8VQKlBJe+3IKOqLC2uWiqXaIQ6gSNQFw1NgAHhZq7DGb+uhP39GKtZgrttTSH/VBADGXq40VM0x7IbPotlF4pPza8AtGfo2m5R3+fBeZcx29Kq2aVIXt8ucTHAgdFtl4lOEGEQYmCUjsbHIatTPF2UjtNembxMaWdsfnlh90nNi4r8ec4nNKj140+C6HSFE1hYfVXYnisgOtE/W/ed4F7N9A+nWofazGZpGPHLSjSiZkTcPQ/m/d1lgSQsJZdXKHlUPt78b73KdxuGUXIOWq+cf3MjsHqeX8TCbHNJQjNriwz3UN10Pu9IM+VHpkpbTVKmFQfhdCwCQTHn/VZMso3yblqpx08KW7Q2BZVm8FKX6mkTSXPFob0A7VvVI/z+KRulDbJ4iGmHO5eJShVie2Z/MRhZjyE94kTtzyIKQwDyP92kVKBzJaJwyBOX534UMGgGIq+LF8zMjn11eCckzfLtYmPUGggPuVDJtVeAkNIVCTaLthnxQ9bY62hjgnNgFkSR5QFqVdaQCE7eFlI7QVlS4GX5aFyK3rr9wtfcvaQDvjz10Gur4L95hV/O4B5tR8VGI01H2J8laaNfDalvXP/WztRspvHS0Z/3CNkd0v3W61eyuDd45REewnl0b4DxFZD4zVnqBizuOKggkHRhm2pvn6VB9prgJpFt52fvUBIjPk7UBkVB3tVqzcNSlmLE1+MnMaLqMZiCkQJKwrQ5MC6XM+x/jg+Aeb8Gr7rEILBgZV3sT3DsjKJpf4zFY/s6qZA7OG4/VWn+hkkIBE+jhTbDjS55JZcHD46LweqnOv3Sc5MwJ1yro3BynxrqUrn6wNeYEaQefrzCc2wu7rArLVu6Pfhkf9FBhTUN1Kwa69Lq10H/ZkAH++Fi6RlKUfmAxh9ZaSF17xO3tOaPQq8sg57zfiYr8bDrvn+H9VrJt8XmjfRCidU86YVJscYXWKjmswJqeRlMMoveDFlMq2RSvssMA3cslqO8s8pnEMtoFLeX1J6p1gO5LQhcm6pXS4rJP4R4z36uf8QXVhM4EN8WV4fUuKLtEtQ8/lNZmZZ1NJUxc1cDpm15tMAtmLLuv6GmcSeP5kQk5vhPeNvxeAmbSGxLNw6OaSHWLKXr03gJhEKkOwW1e/KD9yPNJaYe/8UVftjmSxXIrw1HRO6eedcJetNdmXiuwD1fy/fow9P1sy+k5hEYjXOWP9ClpGkmFyho/1Psb97mInsvr1TtRS2/pD1Uksr39JbLC1KBxJ4hI9h9evRF7/1mi4iIjMO2UIhMLYRBywg3vG4b4ruKWQUrkhs5kKHsbnBVrBYMF8TVC1lDoFgDDF+cIG+PY1/LZFKt9+ITigCpOTdJRby2AE9nxVWe4yGX7QLsjSfu6LgaV3wTz3Y2SaW8fK83/cjIxUuOzTgTWJL2h+5iB1fwKtL9m+B3CCCpBA4b/JTllfnd0MzSbA2Nwk8+ouRPBV8Zszl3GQ8OZFkHhgbgsJRPIBKo4CUor8SebPpAyScjCHTmEZZi/NZhnvp4jWMLivgvATB3wAEcRel+OwhwjyhdsDDP0BVgvPWqitEYV82TgszWgggIaKbXU3+AOPSlcL0pIm84wwuTzAN4zAD9kblDHIMkv6AQHlHoeDwi7xCvLpI0ft51oEhcu7DNC8iIv/utH0dJ/xpt2yjHsqdvUI14kI/zgmnDYx+q5JOAz197WCsUuK9NJybh1tMZVTWRfqb1N+WBeBFLCDhsZLYPLYXhGVCOfvMNjB9UA+rKceLcmYV2+vcXh5vKJ+tz4kKNQRT+cH05Wbegh6jz29C3GZdpLcqDqwjUllnXEq/c09yLqmoh5a1k6ti/1WIsYOtVbgFJNa3GfeOz5EqxFxq+e2SM8T7uqSIvuFRulxu4A/d1Mv7lfPabHfAj4AhIPKkVhptih2oCqyRIkeIKQy++KpiKAX+fKf8vFLsaNfP4cl9T3SxsmgpSpnACX15veXwvAJ2y8GElWlml3stuKn1f7iJIwTRpUBzMCFdMgBKUPXCSdt0TLNWHRYuPSKZvbmKSjvJn/fs5cLI9hLDm6SWC5m6eanWHOkszZ5FirpTXzF4OH3GIc6Y1Ub3VB0dMaA/Mn0RME+0lh+tt3hBLgscuABeQu5czEChYcTu35BfNS2GSI3P/cRe3katdNxWAQX9XcCIvnowSni+LjSeuTlmbv1RSICvPGgBXhWN76/AIWR4t6sPHNAcsCDcpAz8be35IRpXjpId0z0Fad2ft1Z2LJ1oSb3WdrR7VDec9DHrV9Yk3vbxJ+QSEK6XohOPnbBv+84yUeOjBKxkYpRWv47lpEmLmk1OuPEZD9jj/g/1jhxs6lVJOQoXrwI0kfS/QcrD+CRzPnsbxkC+Zd9gfCUg8IWxx16HhLVSbMNnrLTg8AsD0Xv06i+RHi4JSt+huanPqIHu/4IrcnurOZSR31H3Sa5bpGq83JAkmtdi/LqsqyeaX3fPbhrhvfHLyl7zfWNZCcRDn1J4pS004XailVtbwAGGmnWksVrmDGkxHVnPTaMtoz4P3oKXVLin8Z8NqdbB7l/Nxph6YpvdWzGeqgVTkFhTMqu15n2k0cQQPJUri+pjNSEhut5PTkEIJnE2BAURu1TaPEZcEvl1ZyoTiTSZKfsm3tsYtSZhQlLgelUlDcUcfR7isc0FgLrzsxeIF3HE6i9EouQ351HaAh9lPxsrP6rfhAb3rKw0wxrOJTxlFLxkvUNfHG7ZT2l5fE62kXtGbJnHzqczNSbA+oYqkQfQThX/R5LVqG8deFJufRmADmQ/oIjgVUBPYWaA5pJb5suClh/4VrPxFI/StlGjj9t05wopx5BXyRA7d31P4vXXWKdZ2xLyPus+fArsHDAJTy2f946VM0Owl61GHQ1ydvetO2OtxXbpyTXG2ixE+cjNP+o3aF+ID53Nqxc5mnfiMRguZ5rVe/aufJCeZm0LW7Il79p7kDVRyfiPfX/miHIpuKtXx7RAcf44h1ruT3WE60DyyhNU/Q1NX/CvOwgqlUoHsSd22f6bTtKGHrM+5gx7bhRnyCsC2m/e50ZJKQVmVuVxwQzIpf6ySYKjwEMs4SMHp0PJWLP79mGBu0Sgw8/ztyS/6OBRx91AWeCcDlevOL9ev2ngPdPhYkZGKgYNX+UOCRfO2tTSdEi9cUFEgb4tP3AmAO9LfqVM7Mi4R8c3gcJwBcqDMmJ8mQJI6P4EgbUmS8QnSoHoCK2VLlMrdSaOqQNz1vjfdtKNFFpHuFUFxjSfmuN1Pjv95vZaDoMvcJE0g+b/Zq/tAGnVE3+iiOEucMIo2xOi++t0Rx/qDUxDs0Njxcq4H55kiK4f28xlsRVJuhJQW4wT0Bq/Wyi/r7fOpcXw/Ox7QY0vCjSLv8d8gqOXHp4xP08RqDryEGtCT7roWzC9Ki1QhrX4E+kFYP9gGKCw1SCBHBl+a7bvug0xJh9q6eunoU521FilhJnmtRPJzvX4Ro/0uAt0zgB2W9DIdY/7pGFn6nm6vX+/Gk9841Mzu73+uStyJjSkvCHevbj3zkS6bJEFePBGw3JQ+VAOvlfAHtJ7rzykvn48zfrpTOSF4pV4uqFeOnm8HhWtvWA5W/Weyu4h9ujExtEj5ZTmm019bHronJ37dXgU4vsAKVagyiIYMTIlLjf5ausw2BBMh7wneN2pV2DHRqYMcZMazrwMElKKwzCoC6RMtYNArrlwrAndQQfpSo72wcT8NnfN9k6uYz3Bw9ETHu7Serm3dVwck60TcBoUW8D4XGY14afi4cAzcvmguZ9zF4SQPo8zpyePfBE8nTVB4yWqfP3rdbmZ4GnBBmFZ638wTWNMZRchMNozXfYKC49AWvu5cWRpdydEm94d3gm7ODqlsnfa4zVVLfYI+ULt4UpIxcso1dbgw6SFWfUunmTyMjVYY3EK1iDxHaSWR5NvYMq2ZoThRuq3YberYdGhn6ANH6S/mgz6q0Z00ws8yTPKzq+PZ4JEAFTfWlaecBBQ6Kpw0JBJJCP0YdQtvOOjMppo1dowARJZs5Ggu/QRqza//E/dAlDilhP7CnvXfcq9dqIs6SZv0GVCAusXm+zxeAU28Yporrj3C3XH4iLH1ybSwZYBK6OiTuWHEXVxmwLsTPAOIrUh7BZE7gV83zMVzlCLgKzzcYZNCT2Ixis6OBUb89LFGbrNrufHo5zajGJeLu8bkKgkjViTG4zTEbKhmBPpq4SMzsE0ypc9zrZKj+ZfP0a6d05besJmsslUuE431ayU05g7ZDHhE8cijoTVoFYVm9/f94lpD4H6g7KZuMxlGRSl5puDgrOKP7rT9792VkmhvwGugJedVJ0TL9LuNkzBLI2tOXirn089dkMBrZJ1dV4ZH5WXnV/wXmUoHjNtagz5kxodNm+OKX+L4O6lIVQppmOrfCQZgr93ILM/DvToKBNWfIeCN+ze98bf0HderNTA68o+F+Nv4Mb3TYbAbK5ZUyoOhBJ0BqH3x+IvJBEFjTj0RVKQYb3U4XyO/lp4La0Ca3CQ6YdOt0aXQM7mPBWDeFTXeERKRb1Vpo0nUhNI0csDVLPPbZu3eW0ElDIVPcyYqZmqMieFK7qEx8yIn+bP/e4chkc13wSbE2QAJUi5+apZtSd2Rf2xiRPkCVqIOJogACoPuGae6ZMN+yWbAKls1vepjBhzAKc4LpWOh/N12FSY7jG9BDt5F65smfJUyw/xUvyb9F5sCEpKoFvYK5BbnD0U1u0HvRgTk/HvfRfmYGMxoB920DPQkqrGhlWf3yu6o5I6+1scjAaJoIOfwwAM2HYiK640p9dBTrgupn6d4LWeSdvH4JM2egFZvtkQJig19x6HbXC+JrYgw2/Uuloo4x2OfSK3G5OQcNZshLY5+NhM3kHkln99A2Fb83dRMuHDUnhJMGEnFHjA3NboYzkbCK2XvfREvRDVu2Y7NWAXgZDnaVGGY5s9PwUStr2Rr1VDj5tmQR1mEd06S1C+iQSecacRFaoExFfc0UKY0t/Kh044ZkDzBagR/5nHRWUScHcKE6zAF3PENOy9UdJxmLbS12dBgC0sHmukUIeWEDDQ7rPCKxg3E5njVe0+gu0nPmC8GaRKTe/e2IdbPfwurYD++MeuRu9yZouE6HhV+qA0aE6N3QY6cMacQbmihb1fxpP1D8qJmwvuiUMoVCKo02XFT7/yPkJPuw790w0JxT2BSTAI67IaUgtOsPqEuk5ZXwByA2kr58bv28h+reGP8SC0RWHm9BPPDkw4lGmz2GLRB8UvLLm4z5S8zChcNVl9dRzkSokpzOZZNfhJ3hb7/MgttVFbZFxoq7RRBoEkaG55y4jqcpfNiiuL/dyhGLjvUx8OZBrlaTMqsLMJVQOyPybyPAGtBnlJAstlwSgNMNlCDVLP5zTMgBWrckJhhTpZxBG5Xpqbgpbztx+gHo69mYb3IpNaYRWpWJp0hdxIKD+gOrOYe8KmjjtDySA/3BnQLqNHd3WBmTCLoZ8Qx0/zlg6hLxYlx7RFMAlu+YaZSDiRdYYllSekSezJQp4Ym9TLLxvCo5UpdqXwq3zNhpDRMTxWhTxDQUMD1JeMX68M++7L+YsewLf0h/3g+VzfJE/Pqxi0XwmhEIvBaKbM01lJB2BaUneusSjMsfCzRflJ7F2gmygT8oxNAPGvQR5RZJEQfkvv06TMAPGIjeGJDdITP7cEgYfEvbeE0ua/xYjxfISqG3yTehdonjrrEXxhH0YXt2aUq5Onn7esksD4apYCNecZ4zTrfDJTioiqNB8mqg7iivVf1lCPiGkpKOnvi+xV4np/w3B+Au5plViPRwqXplykyXUYAcRIzCsRKZoXhBPy3mldvDzD+f3Jxx+jwfrUVoouI4JTGAaZL8YfLZnuEl6ZP5S4U+DEr+93MI0CfcKuFaRzyjzqSJ2frudl2tKxqujT/XIgItmy4cmEwqZzVkPc4xG8zzHmfNt0o/1waPTtw8sW4oRsiIUL1oUQLZqjifh+Ln2+XkxxgsOMItTomc3H2xKF42PmTCwXG1zfkL/03T+PPVw2Nxe+a95lm997GzBqdMhd04cfPR/SCRT+Z+chy42l9v8Ag3Dbw//u53K4GqXNJkvFc2T93mBkJd4xRFYIYpDLW30Xy5ijIdFBbnB9d1QxHGtZ7fBfjv4xERh+7DpdCKM59vsNGV3NGFgKsilI/Mj1PdRFaXE2eKp73HwR8DnoXoY9aiglKXT630nY10CLFlQKwxomIMx5KoSO3GOMjpkHzxYZUK9x8R+JCwpCoTImMmIKuyVZPU/ML2pz8YepQPxwdPCp25R9pHSCcunbGX9bBSdnUoUXwivqWdcXsbOh3f3/7Lv4Zk7p3E1CLFneNFg/Oia4QHIkwgcQIt+M5TM0dHamEeGM7gX8SoUAmQIz/9+0DdD1iqzrkM80jbOtVQFEiFUDotkJGrMmylYSr5EeyRX1WLggJpkJdZfJ5TMIzVK2qnz0BoIE8FVoSYyRBx4kkVbQ2BGQtM80iUpDUk9/YTwJ9fjRzeql+fqdzitEuqXE/igIO6xbaO3RTxc9Jqe/LXvNJHFaeF+/k7QsuNxcT1ZW2r/udCotuF2wIkGtHLcM6wpvTA5FbH4iQF9069sfJ0OSlccMSjMq6QRwQDj3TPvFyCskbkYDycr9bMU4FSx/yHYGvAhBX4jO2J1qVzuMLOuvrzWaYOBUleLHp/A/LCPpCa7wjYkF+emOtJhieuv0NihCaiGj2xN87Hsb0GP13kJ/wFEAwERvAkuE86DPnG8KwiAhHCRLmbYc9YUPcg6ZM6AyOy74cbqQs/DAy43s9XfZxD5yB9ukGXYxfuFhNYI37ZD5RglSNqC7ygCnQPJMEVlStUZkwMwFtYKlRk/7EayrXAVkdT3/7VRw9eIbWt8gnYkguBpFXvubAQzpBdaZeh2pmmP7UzlropvcnbyjQxB1kRz6nXo0g7dmpoUdzCtpQ8NM7UBDOd4U0okVcdD1v+Be5wDD4ivwlMVgjjHKjsHRneyxUEZwFbwj64kBblHRcLJ3FEoT0hEHFsP0VlQelQxN5C75DWaxBCCtbQ9nJ0VSWHjpXb5IU8f9opCJoEmahV6XWxJmCnMAub7958DIO1GyNuiaZ669xkG4a5OKgzxgcibJV20iR8muc7g2KHkDhlX2XCvpIpH4N1CzqGeu5CdRbfJJVm5mm+j9R3hi2cQtgD80D+UmixblWdDw38WF4utPUaCYIQW4YZAr++UBEZj5MESYipRr25dM7B1ePapTGRBV4dCvUy/qXbfhAI98WIyrDm8YEJd633dHD7dkeIA2F30cU2ABO6Pz5Mlejo7qS9NmJDq2ORVbJIkTl1qxaUtbdTU8Xsg8y3iH21IcKR37R2ZLugBnJjA2dDszGOlwxc9ad7anzViHGSmhlHOqTb4jyf41BZaaLzUIjoOiQowfyLg/JSZZ/wril0GFOG4FhUxKikKmz3OXXtiUCNH5TC6GbOJJKDcBCwf3Ksk3QU5mTHxaCTBIpWvfWpcm7cbVcFLX1XuQ6xDDC32pot4oiV6DMzMiKcJJxlrvh3iDwPvi31ekUxrARlaBshgtwPkt4wXr6N6EDexjDCwNG7KQVWVVUilSxUh2Zo2MO+zgKVLoZJB0kfOb2PZnrW9ktvTSVpwANCnjCTaUnSZ20+tSvTi7Bqx2AuPYOc437JBYf8QYr9yrWHGdQXYyUFC6GrRlwgHKvE2Vb4uDVMX6xv+PoV1aWiXUn/6V4+RfOBvWSt22hQIpug5+rXQKVqisjui6mW7BdD/U/YUqhGC5gSAmCGlgSQ2EXGT+VXH22/pDYEpjPZN2KtwPioPpECO8R59Pr+YenL+eUJmnaLSahuWW6dCMN8O+s9yubPSFZvf0+8tmupBwqQr1zXfR8LhEYNgw3rsFHy304Z4ujL24DpvVP9D8JgAom6mzjzKVJsJMREfJsncE4wJdMTiKCVnidaRZXvuirTABFtl5ZDVRrFYpSdXOLE3z2vcDTTk5EN54eLghI3RcaE9QQDAI47DAOxzGrcRn8v8jRxFP0yzGJPDPv4EPXc9z9E8Np808H5kF8wEWYnJH6R1CLpvgwL3ZfztRvcEdWqySUMPEQDU7QbFOdqa9dqKF84SazF1u/QsV2xrb3QlDSsJF8KotDgg5Yi1ktIpFSK4qYxcdg0f/5MOdL65yjY8Ppt8XpqNpM9m7B5tKRAZ2cfxRfOXOTjQrs3YCfF8SehnPcJrVlrlE5yDXEUim2MC8OuG6sr6aKwYJQw3eUJElwVv39ZgzNglLN6/YqSxSGSRGI6xXHI875AyWKUkcWMEsQub5s+4MaB9RR0O3dRRRimxWTrx9bbF/Xmi3bsc1edFweMUoDRXfii6nBl5vrxzgEnMOqFRxlvm3bZiuXVD3ZKjKbAoK/VsZuMEOyHnEKJx8jLZ6q3p2xWWLEqXz24fqw4r/LmfYf0YzEN48FAb3c4D0lIjelreQBAX6FgLhMf/r/p4T4ReLcC4vowfuc8XdvxG155MHkppeDmucaRKLStqUzD1g0Th7LQfe6Z79SqfDG77XX4pvYyUT8KwcFILb4sPyGL+Um6vuU/O2yGviXCBo9r9b00OJpjJznTjrGDTxZqI+1tEXQYRnzb0yMjtunfI2a37GvBs3G5njb4V8pisgvLuaIdeB0DScr3hsunU8K4tZuwsybHcP8KDpQ=\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/"},"partialJson":"{\"path\":\"/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"home","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home"},"partialJson":"{\"path\":\"/home"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home"},"partialJson":"{\"path\":\"/home"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/al","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/al"},"partialJson":"{\"path\":\"/home/al"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/al"},"partialJson":"{\"path\":\"/home/al"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"ex","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex"},"partialJson":"{\"path\":\"/home/alex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex"},"partialJson":"{\"path\":\"/home/alex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/.","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/."},"partialJson":"{\"path\":\"/home/alex/."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/."},"partialJson":"{\"path\":\"/home/alex/."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"pi","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi"},"partialJson":"{\"path\":\"/home/alex/.pi"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi"},"partialJson":"{\"path\":\"/home/alex/.pi"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/"},"partialJson":"{\"path\":\"/home/alex/.pi/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/"},"partialJson":"{\"path\":\"/home/alex/.pi/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"agent","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent"},"partialJson":"{\"path\":\"/home/alex/.pi/agent"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent"},"partialJson":"{\"path\":\"/home/alex/.pi/agent"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"HDJDumn7iz0UYNSlHvsqi1Ubed1OccKw3W3W4L3FRHXBJiLCYyQjesSHilh3HtnIK8ygtwLkv7RGbr5DVhCGA+jSbEdpq/3K7czhtZSFqkGCweB9XgAX+a6YGPL/a+ccTSXJNDHPBha9qxZIggxNxAw+vqSYNdAn4wKV5boLbLMqPZ8D9HFjaAHjk4ZJlf7VcMKlLxrRLhIflx4ciB4MnG7LxjTUqrgPCvp+bblScvDLua+NfJEEKLAi9pPAjFwQQhAiBJLJPtYUQXqGSuShryxJY8w7y0wTWl7oDDiJz2L1WaOK91yWIF6+ekrMxeytjVePMqL/VEDizSEMXDhJ4EXMVUvz2NNp8WYh+pKgQ+3hpk9GB9MBULkobhmzZx6wgWMF3bPHJ4isb8RGU18K2mUhEqUbrUVwe2seJYgjBcGiz/VG9FFtk9PQ7o4HYOOm42NDle5nG1Y1nsx41jDQLvWVFtgy2lu8FJhtW3k5Yf3atAUt/Fv7cHW1fOF9NeflG8RSWWqQjW+RkRFRlmvDgw0YWyPQH8gNl8VQKlBJe+3IKOqLC2uWiqXaIQ6gSNQFw1NgAHhZq7DGb+uhP39GKtZgrttTSH/VBADGXq40VM0x7IbPotlF4pPza8AtGfo2m5R3+fBeZcx29Kq2aVIXt8ucTHAgdFtl4lOEGEQYmCUjsbHIatTPF2UjtNembxMaWdsfnlh90nNi4r8ec4nNKj140+C6HSFE1hYfVXYnisgOtE/W/ed4F7N9A+nWofazGZpGPHLSjSiZkTcPQ/m/d1lgSQsJZdXKHlUPt78b73KdxuGUXIOWq+cf3MjsHqeX8TCbHNJQjNriwz3UN10Pu9IM+VHpkpbTVKmFQfhdCwCQTHn/VZMso3yblqpx08KW7Q2BZVm8FKX6mkTSXPFob0A7VvVI/z+KRulDbJ4iGmHO5eJShVie2Z/MRhZjyE94kTtzyIKQwDyP92kVKBzJaJwyBOX534UMGgGIq+LF8zMjn11eCckzfLtYmPUGggPuVDJtVeAkNIVCTaLthnxQ9bY62hjgnNgFkSR5QFqVdaQCE7eFlI7QVlS4GX5aFyK3rr9wtfcvaQDvjz10Gur4L95hV/O4B5tR8VGI01H2J8laaNfDalvXP/WztRspvHS0Z/3CNkd0v3W61eyuDd45REewnl0b4DxFZD4zVnqBizuOKggkHRhm2pvn6VB9prgJpFt52fvUBIjPk7UBkVB3tVqzcNSlmLE1+MnMaLqMZiCkQJKwrQ5MC6XM+x/jg+Aeb8Gr7rEILBgZV3sT3DsjKJpf4zFY/s6qZA7OG4/VWn+hkkIBE+jhTbDjS55JZcHD46LweqnOv3Sc5MwJ1yro3BynxrqUrn6wNeYEaQefrzCc2wu7rArLVu6Pfhkf9FBhTUN1Kwa69Lq10H/ZkAH++Fi6RlKUfmAxh9ZaSF17xO3tOaPQq8sg57zfiYr8bDrvn+H9VrJt8XmjfRCidU86YVJscYXWKjmswJqeRlMMoveDFlMq2RSvssMA3cslqO8s8pnEMtoFLeX1J6p1gO5LQhcm6pXS4rJP4R4z36uf8QXVhM4EN8WV4fUuKLtEtQ8/lNZmZZ1NJUxc1cDpm15tMAtmLLuv6GmcSeP5kQk5vhPeNvxeAmbSGxLNw6OaSHWLKXr03gJhEKkOwW1e/KD9yPNJaYe/8UVftjmSxXIrw1HRO6eedcJetNdmXiuwD1fy/fow9P1sy+k5hEYjXOWP9ClpGkmFyho/1Psb97mInsvr1TtRS2/pD1Uksr39JbLC1KBxJ4hI9h9evRF7/1mi4iIjMO2UIhMLYRBywg3vG4b4ruKWQUrkhs5kKHsbnBVrBYMF8TVC1lDoFgDDF+cIG+PY1/LZFKt9+ITigCpOTdJRby2AE9nxVWe4yGX7QLsjSfu6LgaV3wTz3Y2SaW8fK83/cjIxUuOzTgTWJL2h+5iB1fwKtL9m+B3CCCpBA4b/JTllfnd0MzSbA2Nwk8+ouRPBV8Zszl3GQ8OZFkHhgbgsJRPIBKo4CUor8SebPpAyScjCHTmEZZi/NZhnvp4jWMLivgvATB3wAEcRel+OwhwjyhdsDDP0BVgvPWqitEYV82TgszWgggIaKbXU3+AOPSlcL0pIm84wwuTzAN4zAD9kblDHIMkv6AQHlHoeDwi7xCvLpI0ft51oEhcu7DNC8iIv/utH0dJ/xpt2yjHsqdvUI14kI/zgmnDYx+q5JOAz197WCsUuK9NJybh1tMZVTWRfqb1N+WBeBFLCDhsZLYPLYXhGVCOfvMNjB9UA+rKceLcmYV2+vcXh5vKJ+tz4kKNQRT+cH05Wbegh6jz29C3GZdpLcqDqwjUllnXEq/c09yLqmoh5a1k6ti/1WIsYOtVbgFJNa3GfeOz5EqxFxq+e2SM8T7uqSIvuFRulxu4A/d1Mv7lfPabHfAj4AhIPKkVhptih2oCqyRIkeIKQy++KpiKAX+fKf8vFLsaNfP4cl9T3SxsmgpSpnACX15veXwvAJ2y8GElWlml3stuKn1f7iJIwTRpUBzMCFdMgBKUPXCSdt0TLNWHRYuPSKZvbmKSjvJn/fs5cLI9hLDm6SWC5m6eanWHOkszZ5FirpTXzF4OH3GIc6Y1Ub3VB0dMaA/Mn0RME+0lh+tt3hBLgscuABeQu5czEChYcTu35BfNS2GSI3P/cRe3katdNxWAQX9XcCIvnowSni+LjSeuTlmbv1RSICvPGgBXhWN76/AIWR4t6sPHNAcsCDcpAz8be35IRpXjpId0z0Fad2ft1Z2LJ1oSb3WdrR7VDec9DHrV9Yk3vbxJ+QSEK6XohOPnbBv+84yUeOjBKxkYpRWv47lpEmLmk1OuPEZD9jj/g/1jhxs6lVJOQoXrwI0kfS/QcrD+CRzPnsbxkC+Zd9gfCUg8IWxx16HhLVSbMNnrLTg8AsD0Xv06i+RHi4JSt+huanPqIHu/4IrcnurOZSR31H3Sa5bpGq83JAkmtdi/LqsqyeaX3fPbhrhvfHLyl7zfWNZCcRDn1J4pS004XailVtbwAGGmnWksVrmDGkxHVnPTaMtoz4P3oKXVLin8Z8NqdbB7l/Nxph6YpvdWzGeqgVTkFhTMqu15n2k0cQQPJUri+pjNSEhut5PTkEIJnE2BAURu1TaPEZcEvl1ZyoTiTSZKfsm3tsYtSZhQlLgelUlDcUcfR7isc0FgLrzsxeIF3HE6i9EouQ351HaAh9lPxsrP6rfhAb3rKw0wxrOJTxlFLxkvUNfHG7ZT2l5fE62kXtGbJnHzqczNSbA+oYqkQfQThX/R5LVqG8deFJufRmADmQ/oIjgVUBPYWaA5pJb5suClh/4VrPxFI/StlGjj9t05wopx5BXyRA7d31P4vXXWKdZ2xLyPus+fArsHDAJTy2f946VM0Owl61GHQ1ydvetO2OtxXbpyTXG2ixE+cjNP+o3aF+ID53Nqxc5mnfiMRguZ5rVe/aufJCeZm0LW7Il79p7kDVRyfiPfX/miHIpuKtXx7RAcf44h1ruT3WE60DyyhNU/Q1NX/CvOwgqlUoHsSd22f6bTtKGHrM+5gx7bhRnyCsC2m/e50ZJKQVmVuVxwQzIpf6ySYKjwEMs4SMHp0PJWLP79mGBu0Sgw8/ztyS/6OBRx91AWeCcDlevOL9ev2ngPdPhYkZGKgYNX+UOCRfO2tTSdEi9cUFEgb4tP3AmAO9LfqVM7Mi4R8c3gcJwBcqDMmJ8mQJI6P4EgbUmS8QnSoHoCK2VLlMrdSaOqQNz1vjfdtKNFFpHuFUFxjSfmuN1Pjv95vZaDoMvcJE0g+b/Zq/tAGnVE3+iiOEucMIo2xOi++t0Rx/qDUxDs0Njxcq4H55kiK4f28xlsRVJuhJQW4wT0Bq/Wyi/r7fOpcXw/Ox7QY0vCjSLv8d8gqOXHp4xP08RqDryEGtCT7roWzC9Ki1QhrX4E+kFYP9gGKCw1SCBHBl+a7bvug0xJh9q6eunoU521FilhJnmtRPJzvX4Ro/0uAt0zgB2W9DIdY/7pGFn6nm6vX+/Gk9841Mzu73+uStyJjSkvCHevbj3zkS6bJEFePBGw3JQ+VAOvlfAHtJ7rzykvn48zfrpTOSF4pV4uqFeOnm8HhWtvWA5W/Weyu4h9ujExtEj5ZTmm019bHronJ37dXgU4vsAKVagyiIYMTIlLjf5ausw2BBMh7wneN2pV2DHRqYMcZMazrwMElKKwzCoC6RMtYNArrlwrAndQQfpSo72wcT8NnfN9k6uYz3Bw9ETHu7Serm3dVwck60TcBoUW8D4XGY14afi4cAzcvmguZ9zF4SQPo8zpyePfBE8nTVB4yWqfP3rdbmZ4GnBBmFZ638wTWNMZRchMNozXfYKC49AWvu5cWRpdydEm94d3gm7ODqlsnfa4zVVLfYI+ULt4UpIxcso1dbgw6SFWfUunmTyMjVYY3EK1iDxHaSWR5NvYMq2ZoThRuq3YberYdGhn6ANH6S/mgz6q0Z00ws8yTPKzq+PZ4JEAFTfWlaecBBQ6Kpw0JBJJCP0YdQtvOOjMppo1dowARJZs5Ggu/QRqza//E/dAlDilhP7CnvXfcq9dqIs6SZv0GVCAusXm+zxeAU28Yporrj3C3XH4iLH1ybSwZYBK6OiTuWHEXVxmwLsTPAOIrUh7BZE7gV83zMVzlCLgKzzcYZNCT2Ixis6OBUb89LFGbrNrufHo5zajGJeLu8bkKgkjViTG4zTEbKhmBPpq4SMzsE0ypc9zrZKj+ZfP0a6d05besJmsslUuE431ayU05g7ZDHhE8cijoTVoFYVm9/f94lpD4H6g7KZuMxlGRSl5puDgrOKP7rT9792VkmhvwGugJedVJ0TL9LuNkzBLI2tOXirn089dkMBrZJ1dV4ZH5WXnV/wXmUoHjNtagz5kxodNm+OKX+L4O6lIVQppmOrfCQZgr93ILM/DvToKBNWfIeCN+ze98bf0HderNTA68o+F+Nv4Mb3TYbAbK5ZUyoOhBJ0BqH3x+IvJBEFjTj0RVKQYb3U4XyO/lp4La0Ca3CQ6YdOt0aXQM7mPBWDeFTXeERKRb1Vpo0nUhNI0csDVLPPbZu3eW0ElDIVPcyYqZmqMieFK7qEx8yIn+bP/e4chkc13wSbE2QAJUi5+apZtSd2Rf2xiRPkCVqIOJogACoPuGae6ZMN+yWbAKls1vepjBhzAKc4LpWOh/N12FSY7jG9BDt5F65smfJUyw/xUvyb9F5sCEpKoFvYK5BbnD0U1u0HvRgTk/HvfRfmYGMxoB920DPQkqrGhlWf3yu6o5I6+1scjAaJoIOfwwAM2HYiK640p9dBTrgupn6d4LWeSdvH4JM2egFZvtkQJig19x6HbXC+JrYgw2/Uuloo4x2OfSK3G5OQcNZshLY5+NhM3kHkln99A2Fb83dRMuHDUnhJMGEnFHjA3NboYzkbCK2XvfREvRDVu2Y7NWAXgZDnaVGGY5s9PwUStr2Rr1VDj5tmQR1mEd06S1C+iQSecacRFaoExFfc0UKY0t/Kh044ZkDzBagR/5nHRWUScHcKE6zAF3PENOy9UdJxmLbS12dBgC0sHmukUIeWEDDQ7rPCKxg3E5njVe0+gu0nPmC8GaRKTe/e2IdbPfwurYD++MeuRu9yZouE6HhV+qA0aE6N3QY6cMacQbmihb1fxpP1D8qJmwvuiUMoVCKo02XFT7/yPkJPuw790w0JxT2BSTAI67IaUgtOsPqEuk5ZXwByA2kr58bv28h+reGP8SC0RWHm9BPPDkw4lGmz2GLRB8UvLLm4z5S8zChcNVl9dRzkSokpzOZZNfhJ3hb7/MgttVFbZFxoq7RRBoEkaG55y4jqcpfNiiuL/dyhGLjvUx8OZBrlaTMqsLMJVQOyPybyPAGtBnlJAstlwSgNMNlCDVLP5zTMgBWrckJhhTpZxBG5Xpqbgpbztx+gHo69mYb3IpNaYRWpWJp0hdxIKD+gOrOYe8KmjjtDySA/3BnQLqNHd3WBmTCLoZ8Qx0/zlg6hLxYlx7RFMAlu+YaZSDiRdYYllSekSezJQp4Ym9TLLxvCo5UpdqXwq3zNhpDRMTxWhTxDQUMD1JeMX68M++7L+YsewLf0h/3g+VzfJE/Pqxi0XwmhEIvBaKbM01lJB2BaUneusSjMsfCzRflJ7F2gmygT8oxNAPGvQR5RZJEQfkvv06TMAPGIjeGJDdITP7cEgYfEvbeE0ua/xYjxfISqG3yTehdonjrrEXxhH0YXt2aUq5Onn7esksD4apYCNecZ4zTrfDJTioiqNB8mqg7iivVf1lCPiGkpKOnvi+xV4np/w3B+Au5plViPRwqXplykyXUYAcRIzCsRKZoXhBPy3mldvDzD+f3Jxx+jwfrUVoouI4JTGAaZL8YfLZnuEl6ZP5S4U+DEr+93MI0CfcKuFaRzyjzqSJ2frudl2tKxqujT/XIgItmy4cmEwqZzVkPc4xG8zzHmfNt0o/1waPTtw8sW4oRsiIUL1oUQLZqjifh+Ln2+XkxxgsOMItTomc3H2xKF42PmTCwXG1zfkL/03T+PPVw2Nxe+a95lm997GzBqdMhd04cfPR/SCRT+Z+chy42l9v8Ag3Dbw//u53K4GqXNJkvFc2T93mBkJd4xRFYIYpDLW30Xy5ijIdFBbnB9d1QxHGtZ7fBfjv4xERh+7DpdCKM59vsNGV3NGFgKsilI/Mj1PdRFaXE2eKp73HwR8DnoXoY9aiglKXT630nY10CLFlQKwxomIMx5KoSO3GOMjpkHzxYZUK9x8R+JCwpCoTImMmIKuyVZPU/ML2pz8YepQPxwdPCp25R9pHSCcunbGX9bBSdnUoUXwivqWdcXsbOh3f3/7Lv4Zk7p3E1CLFneNFg/Oia4QHIkwgcQIt+M5TM0dHamEeGM7gX8SoUAmQIz/9+0DdD1iqzrkM80jbOtVQFEiFUDotkJGrMmylYSr5EeyRX1WLggJpkJdZfJ5TMIzVK2qnz0BoIE8FVoSYyRBx4kkVbQ2BGQtM80iUpDUk9/YTwJ9fjRzeql+fqdzitEuqXE/igIO6xbaO3RTxc9Jqe/LXvNJHFaeF+/k7QsuNxcT1ZW2r/udCotuF2wIkGtHLcM6wpvTA5FbH4iQF9069sfJ0OSlccMSjMq6QRwQDj3TPvFyCskbkYDycr9bMU4FSx/yHYGvAhBX4jO2J1qVzuMLOuvrzWaYOBUleLHp/A/LCPpCa7wjYkF+emOtJhieuv0NihCaiGj2xN87Hsb0GP13kJ/wFEAwERvAkuE86DPnG8KwiAhHCRLmbYc9YUPcg6ZM6AyOy74cbqQs/DAy43s9XfZxD5yB9ukGXYxfuFhNYI37ZD5RglSNqC7ygCnQPJMEVlStUZkwMwFtYKlRk/7EayrXAVkdT3/7VRw9eIbWt8gnYkguBpFXvubAQzpBdaZeh2pmmP7UzlropvcnbyjQxB1kRz6nXo0g7dmpoUdzCtpQ8NM7UBDOd4U0okVcdD1v+Be5wDD4ivwlMVgjjHKjsHRneyxUEZwFbwj64kBblHRcLJ3FEoT0hEHFsP0VlQelQxN5C75DWaxBCCtbQ9nJ0VSWHjpXb5IU8f9opCJoEmahV6XWxJmCnMAub7958DIO1GyNuiaZ669xkG4a5OKgzxgcibJV20iR8muc7g2KHkDhlX2XCvpIpH4N1CzqGeu5CdRbfJJVm5mm+j9R3hi2cQtgD80D+UmixblWdDw38WF4utPUaCYIQW4YZAr++UBEZj5MESYipRr25dM7B1ePapTGRBV4dCvUy/qXbfhAI98WIyrDm8YEJd633dHD7dkeIA2F30cU2ABO6Pz5Mlejo7qS9NmJDq2ORVbJIkTl1qxaUtbdTU8Xsg8y3iH21IcKR37R2ZLugBnJjA2dDszGOlwxc9ad7anzViHGSmhlHOqTb4jyf41BZaaLzUIjoOiQowfyLg/JSZZ/wril0GFOG4FhUxKikKmz3OXXtiUCNH5TC6GbOJJKDcBCwf3Ksk3QU5mTHxaCTBIpWvfWpcm7cbVcFLX1XuQ6xDDC32pot4oiV6DMzMiKcJJxlrvh3iDwPvi31ekUxrARlaBshgtwPkt4wXr6N6EDexjDCwNG7KQVWVVUilSxUh2Zo2MO+zgKVLoZJB0kfOb2PZnrW9ktvTSVpwANCnjCTaUnSZ20+tSvTi7Bqx2AuPYOc437JBYf8QYr9yrWHGdQXYyUFC6GrRlwgHKvE2Vb4uDVMX6xv+PoV1aWiXUn/6V4+RfOBvWSt22hQIpug5+rXQKVqisjui6mW7BdD/U/YUqhGC5gSAmCGlgSQ2EXGT+VXH22/pDYEpjPZN2KtwPioPpECO8R59Pr+YenL+eUJmnaLSahuWW6dCMN8O+s9yubPSFZvf0+8tmupBwqQr1zXfR8LhEYNgw3rsFHy304Z4ujL24DpvVP9D8JgAom6mzjzKVJsJMREfJsncE4wJdMTiKCVnidaRZXvuirTABFtl5ZDVRrFYpSdXOLE3z2vcDTTk5EN54eLghI3RcaE9QQDAI47DAOxzGrcRn8v8jRxFP0yzGJPDPv4EPXc9z9E8Np808H5kF8wEWYnJH6R1CLpvgwL3ZfztRvcEdWqySUMPEQDU7QbFOdqa9dqKF84SazF1u/QsV2xrb3QlDSsJF8KotDgg5Yi1ktIpFSK4qYxcdg0f/5MOdL65yjY8Ppt8XpqNpM9m7B5tKRAZ2cfxRfOXOTjQrs3YCfF8SehnPcJrVlrlE5yDXEUim2MC8OuG6sr6aKwYJQw3eUJElwVv39ZgzNglLN6/YqSxSGSRGI6xXHI875AyWKUkcWMEsQub5s+4MaB9RR0O3dRRRimxWTrx9bbF/Xmi3bsc1edFweMUoDRXfii6nBl5vrxzgEnMOqFRxlvm3bZiuXVD3ZKjKbAoK/VsZuMEOyHnEKJx8jLZ6q3p2xWWLEqXz24fqw4r/LmfYf0YzEN48FAb3c4D0lIjelreQBAX6FgLhMf/r/p4T4ReLcC4vowfuc8XdvxG155MHkppeDmucaRKLStqUzD1g0Th7LQfe6Z79SqfDG77XX4pvYyUT8KwcFILb4sPyGL+Um6vuU/O2yGviXCBo9r9b00OJpjJznTjrGDTxZqI+1tEXQYRnzb0yMjtunfI2a37GvBs3G5njb4V8pisgvLuaIdeB0DScr3hsunU8K4tZuwsybHcP8KDpQ=\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/a","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/a"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/a"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"udit","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/S","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit/S"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/audit/S"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit/S"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/audit/S"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"K","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit/SK"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/audit/SK"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit/SK"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/audit/SK"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"ILL","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit/SKILL"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/audit/SKILL"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit/SKILL"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/audit/SKILL"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":".md","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"\"}","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_end","contentIndex":1,"toolCall":{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|4NqiNwUkrfuewD0oE2KaORnwsUD38ZTbCOW4ohPlp4mosopG0UnlRp/N1q65tJmCVYOJcVQzWdb1iTA17lcx8zoZyjX9/0h4wgFgsQ0/ULOwNxp9mo4oNMl7fJvYuB/QULXocj+DGAqSji3MLR3Xh3rnZl12TH10Hc034Bl4KBX781dUWL+4N+kI/lnDChymN6iTbt7xZdqYfrUy1jO0Wj1UyLfaYurmHwDryaKhP27TpaVdW0KRqhSOecAz45/A0x/HudcyF08H1arJyzFp7SwN66nGY+sYz8BCynLHlpxxropXTM1KzJAoaB1ZagO6zfQb+l3Ds4OFV92zdMUF5mY+LeXV+5B/PRAGonMJOF1D51lS34gGb8MvJpPPmXvXv3VW2eDLx+FgbR6/lbaRzB8yFw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md"}},"partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940974429,"responseId":"VEbtVJNQlh/HEzwJeSH1Ex65aq6wm7HxdF1Csfr+4wYblQxsRWG3jwgr4mbxprOffFAZ1GoMUwCqeCiuW0sHHf7wx/Ahqrp+Ze1jA8Unn7pq5eUhcWS11lmdJG1+kc8uB0LB2jI/aIBzcvH3L/dKNj9xS/2zZJM1gt664Qi42rI7e+BwJDNgNBnCPqY9mx9b80Bvh+xlxm1Ex/jgHLb80NuOn4J/SETg17kOz+1m6o1fbkLYW63odaQPbJxYsIUSPc6Trl97m8llaTR3RPboNIP6ipvqFTorR9oxIgXUhHvnrBlpDZ0pSQ+i3OHZeqw84FFktdoPYpmXqw3nZTexXAoYsnZZT0vFZfx9wwfwxH4SE00g73MtPXLJTEeA780uA0Wxd77oULNN2MvaYsTkOirBws4t"}}
{"type":"message_end","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":484,"output":962,"cacheRead":2560,"cacheWrite":0,"totalTokens":4006,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775940974429,"responseId":"ne8qzSbGOSg4QPKPAMgRtHxbYLUNtGitqhqLzZz0CG6IO5y2TWDwBM0AZnY0iUal1NNkCwx7DtdEu0Q2k4mCfrpPv1YlSm4UbrxdJHT7iXPbUiqAhB7pAUwIl6+hyb0cRxnyH8IP3ns7B5gjWChkdUqavcREgklue+pqo+vKFJkIPus8w8A+VFAKh/EydmsuVgrjG8pAmWFsX9FX0rgqwk7zniYOmGknmG3+60dpCVQxXB6HzBTdWXRbNMPpozHtEFdyWDa0Bx19yEmXDghO/cRro7ZkcgKIHfsHki7JIpFlVyyLX/2UGpVcRPQWaqeRIeEAzjtCGz3zwQBEJxK6kVxKM2TNRJKQd+ZmvZnyNUGsNUq7NQ+tRJK9ytJb0TiJwUbkmZ3W2u+qSJAiwiBowlyFcCc6"}}
{"type":"tool_execution_start","toolCallId":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","toolName":"read","args":{"path":"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md"}}
{"type":"tool_execution_end","toolCallId":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","toolName":"read","result":{"content":[{"type":"text","text":"---\nname: audit\ndescription: Run technical quality checks across accessibility, performance, theming, responsive design, and anti-patterns. Generates a scored report with P0-P3 severity ratings and actionable plan. Use when the user wants an accessibility check, performance audit, or technical quality review.\n---\n\n## MANDATORY PREPARATION\n\nInvoke /frontend-design — it contains design principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first.\n\n---\n\nRun systematic **technical** quality checks and generate a comprehensive report. Don't fix issues — document them for other commands to address.\n\nThis is a code-level audit, not a design critique. Check what's measurable and verifiable in the implementation.\n\n## Diagnostic Scan\n\nRun comprehensive checks across 5 dimensions. Score each dimension 0-4 using the criteria below.\n\n### 1. Accessibility (A11y)\n\n**Check for**:\n- **Contrast issues**: Text contrast ratios < 4.5:1 (or 7:1 for AAA)\n- **Missing ARIA**: Interactive elements without proper roles, labels, or states\n- **Keyboard navigation**: Missing focus indicators, illogical tab order, keyboard traps\n- **Semantic HTML**: Improper heading hierarchy, missing landmarks, divs instead of buttons\n- **Alt text**: Missing or poor image descriptions\n- **Form issues**: Inputs without labels, poor error messaging, missing required indicators\n\n**Score 0-4**: 0=Inaccessible (fails WCAG A), 1=Major gaps (few ARIA labels, no keyboard nav), 2=Partial (some a11y effort, significant gaps), 3=Good (WCAG AA mostly met, minor gaps), 4=Excellent (WCAG AA fully met, approaches AAA)\n\n### 2. Performance\n\n**Check for**:\n- **Layout thrashing**: Reading/writing layout properties in loops\n- **Expensive animations**: Animating layout properties (width, height, top, left) instead of transform/opacity\n- **Missing optimization**: Images without lazy loading, unoptimized assets, missing will-change\n- **Bundle size**: Unnecessary imports, unused dependencies\n- **Render performance**: Unnecessary re-renders, missing memoization\n\n**Score 0-4**: 0=Severe issues (layout thrash, unoptimized everything), 1=Major problems (no lazy loading, expensive animations), 2=Partial (some optimization, gaps remain), 3=Good (mostly optimized, minor improvements possible), 4=Excellent (fast, lean, well-optimized)\n\n### 3. Theming\n\n**Check for**:\n- **Hard-coded colors**: Colors not using design tokens\n- **Broken dark mode**: Missing dark mode variants, poor contrast in dark theme\n- **Inconsistent tokens**: Using wrong tokens, mixing token types\n- **Theme switching issues**: Values that don't update on theme change\n\n**Score 0-4**: 0=No theming (hard-coded everything), 1=Minimal tokens (mostly hard-coded), 2=Partial (tokens exist but inconsistently used), 3=Good (tokens used, minor hard-coded values), 4=Excellent (full token system, dark mode works perfectly)\n\n### 4. Responsive Design\n\n**Check for**:\n- **Fixed widths**: Hard-coded widths that break on mobile\n- **Touch targets**: Interactive elements < 44x44px\n- **Horizontal scroll**: Content overflow on narrow viewports\n- **Text scaling**: Layouts that break when text size increases\n- **Missing breakpoints**: No mobile/tablet variants\n\n**Score 0-4**: 0=Desktop-only (breaks on mobile), 1=Major issues (some breakpoints, many failures), 2=Partial (works on mobile, rough edges), 3=Good (responsive, minor touch target or overflow issues), 4=Excellent (fluid, all viewports, proper touch targets)\n\n### 5. Anti-Patterns (CRITICAL)\n\nCheck against ALL the **DON'T** guidelines in the frontend-design skill. Look for AI slop tells (AI color palette, gradient text, glassmorphism, hero metrics, card grids, generic fonts) and general design anti-patterns (gray on color, nested cards, bounce easing, redundant copy).\n\n**Score 0-4**: 0=AI slop gallery (5+ tells), 1=Heavy AI aesthetic (3-4 tells), 2=Some tells (1-2 noticeable), 3=Mostly clean (subtle issues only), 4=No AI tells (distinctive, intentional design)\n\n## Generate Report\n\n### Audit Health Score\n\n| # | Dimension | Score | Key Finding |\n|---|-----------|-------|-------------|\n| 1 | Accessibility | ? | [most critical a11y issue or \"--\"] |\n| 2 | Performance | ? | |\n| 3 | Responsive Design | ? | |\n| 4 | Theming | ? | |\n| 5 | Anti-Patterns | ? | |\n| **Total** | | **??/20** | **[Rating band]** |\n\n**Rating bands**: 18-20 Excellent (minor polish), 14-17 Good (address weak dimensions), 10-13 Acceptable (significant work needed), 6-9 Poor (major overhaul), 0-5 Critical (fundamental issues)\n\n### Anti-Patterns Verdict\n**Start here.** Pass/fail: Does this look AI-generated? List specific tells. Be brutally honest.\n\n### Executive Summary\n- Audit Health Score: **??/20** ([rating band])\n- Total issues found (count by severity: P0/P1/P2/P3)\n- Top 3-5 critical issues\n- Recommended next steps\n\n### Detailed Findings by Severity\n\nTag every issue with **P0-P3 severity**:\n- **P0 Blocking**: Prevents task completion — fix immediately\n- **P1 Major**: Significant difficulty or WCAG AA violation — fix before release\n- **P2 Minor**: Annoyance, workaround exists — fix in next pass\n- **P3 Polish**: Nice-to-fix, no real user impact — fix if time permits\n\nFor each issue, document:\n- **[P?] Issue name**\n- **Location**: Component, file, line\n- **Category**: Accessibility / Performance / Theming / Responsive / Anti-Pattern\n- **Impact**: How it affects users\n- **WCAG/Standard**: Which standard it violates (if applicable)\n- **Recommendation**: How to fix it\n- **Suggested command**: Which command to use (prefer: /animate, /quieter, /optimize, /adapt, /clarify, /distill, /delight, /onboard, /normalize, /audit, /harden, /polish, /extract, /bolder, /arrange, /typeset, /critique, /colorize, /overdrive)\n\n### Patterns & Systemic Issues\n\nIdentify recurring problems that indicate systemic gaps rather than one-off mistakes:\n- \"Hard-coded colors appear in 15+ components, should use design tokens\"\n- \"Touch targets consistently too small (<44px) throughout mobile experience\"\n\n### Positive Findings\n\nNote what's working well — good practices to maintain and replicate.\n\n## Recommended Actions\n\nList recommended commands in priority order (P0 first, then P1, then P2):\n\n1. **[P?] `/command-name`** — Brief description (specific context from audit findings)\n2. **[P?] `/command-name`** — Brief description (specific context)\n\n**Rules**: Only recommend commands from: /animate, /quieter, /optimize, /adapt, /clarify, /distill, /delight, /onboard, /normalize, /audit, /harden, /polish, /extract, /bolder, /arrange, /typeset, /critique, /colorize, /overdrive. Map findings to the most appropriate command. End with `/polish` as the final step if any fixes were recommended.\n\nAfter presenting the summary, tell the user:\n\n> You can ask me to run these one at a time, all at once, or in any order you prefer.\n>\n> Re-run `/audit` after fixes to see your score improve.\n\n**IMPORTANT**: Be thorough but actionable. Too many P3 issues creates noise. Focus on what actually matters.\n\n**NEVER**:\n- Report issues without explaining impact (why does this matter?)\n- Provide generic recommendations (be specific and actionable)\n- Skip positive findings (celebrate what works)\n- Forget to prioritize (everything can't be P0)\n- Report false positives without verification\n\nRemember: You're a technical quality auditor. Document systematically, prioritize ruthlessly, cite specific code locations, and provide clear paths to improvement."}]},"isError":false}
{"type":"message_start","message":{"role":"toolResult","toolCallId":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","toolName":"read","content":[{"type":"text","text":"---\nname: audit\ndescription: Run technical quality checks across accessibility, performance, theming, responsive design, and anti-patterns. Generates a scored report with P0-P3 severity ratings and actionable plan. Use when the user wants an accessibility check, performance audit, or technical quality review.\n---\n\n## MANDATORY PREPARATION\n\nInvoke /frontend-design — it contains design principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first.\n\n---\n\nRun systematic **technical** quality checks and generate a comprehensive report. Don't fix issues — document them for other commands to address.\n\nThis is a code-level audit, not a design critique. Check what's measurable and verifiable in the implementation.\n\n## Diagnostic Scan\n\nRun comprehensive checks across 5 dimensions. Score each dimension 0-4 using the criteria below.\n\n### 1. Accessibility (A11y)\n\n**Check for**:\n- **Contrast issues**: Text contrast ratios < 4.5:1 (or 7:1 for AAA)\n- **Missing ARIA**: Interactive elements without proper roles, labels, or states\n- **Keyboard navigation**: Missing focus indicators, illogical tab order, keyboard traps\n- **Semantic HTML**: Improper heading hierarchy, missing landmarks, divs instead of buttons\n- **Alt text**: Missing or poor image descriptions\n- **Form issues**: Inputs without labels, poor error messaging, missing required indicators\n\n**Score 0-4**: 0=Inaccessible (fails WCAG A), 1=Major gaps (few ARIA labels, no keyboard nav), 2=Partial (some a11y effort, significant gaps), 3=Good (WCAG AA mostly met, minor gaps), 4=Excellent (WCAG AA fully met, approaches AAA)\n\n### 2. Performance\n\n**Check for**:\n- **Layout thrashing**: Reading/writing layout properties in loops\n- **Expensive animations**: Animating layout properties (width, height, top, left) instead of transform/opacity\n- **Missing optimization**: Images without lazy loading, unoptimized assets, missing will-change\n- **Bundle size**: Unnecessary imports, unused dependencies\n- **Render performance**: Unnecessary re-renders, missing memoization\n\n**Score 0-4**: 0=Severe issues (layout thrash, unoptimized everything), 1=Major problems (no lazy loading, expensive animations), 2=Partial (some optimization, gaps remain), 3=Good (mostly optimized, minor improvements possible), 4=Excellent (fast, lean, well-optimized)\n\n### 3. Theming\n\n**Check for**:\n- **Hard-coded colors**: Colors not using design tokens\n- **Broken dark mode**: Missing dark mode variants, poor contrast in dark theme\n- **Inconsistent tokens**: Using wrong tokens, mixing token types\n- **Theme switching issues**: Values that don't update on theme change\n\n**Score 0-4**: 0=No theming (hard-coded everything), 1=Minimal tokens (mostly hard-coded), 2=Partial (tokens exist but inconsistently used), 3=Good (tokens used, minor hard-coded values), 4=Excellent (full token system, dark mode works perfectly)\n\n### 4. Responsive Design\n\n**Check for**:\n- **Fixed widths**: Hard-coded widths that break on mobile\n- **Touch targets**: Interactive elements < 44x44px\n- **Horizontal scroll**: Content overflow on narrow viewports\n- **Text scaling**: Layouts that break when text size increases\n- **Missing breakpoints**: No mobile/tablet variants\n\n**Score 0-4**: 0=Desktop-only (breaks on mobile), 1=Major issues (some breakpoints, many failures), 2=Partial (works on mobile, rough edges), 3=Good (responsive, minor touch target or overflow issues), 4=Excellent (fluid, all viewports, proper touch targets)\n\n### 5. Anti-Patterns (CRITICAL)\n\nCheck against ALL the **DON'T** guidelines in the frontend-design skill. Look for AI slop tells (AI color palette, gradient text, glassmorphism, hero metrics, card grids, generic fonts) and general design anti-patterns (gray on color, nested cards, bounce easing, redundant copy).\n\n**Score 0-4**: 0=AI slop gallery (5+ tells), 1=Heavy AI aesthetic (3-4 tells), 2=Some tells (1-2 noticeable), 3=Mostly clean (subtle issues only), 4=No AI tells (distinctive, intentional design)\n\n## Generate Report\n\n### Audit Health Score\n\n| # | Dimension | Score | Key Finding |\n|---|-----------|-------|-------------|\n| 1 | Accessibility | ? | [most critical a11y issue or \"--\"] |\n| 2 | Performance | ? | |\n| 3 | Responsive Design | ? | |\n| 4 | Theming | ? | |\n| 5 | Anti-Patterns | ? | |\n| **Total** | | **??/20** | **[Rating band]** |\n\n**Rating bands**: 18-20 Excellent (minor polish), 14-17 Good (address weak dimensions), 10-13 Acceptable (significant work needed), 6-9 Poor (major overhaul), 0-5 Critical (fundamental issues)\n\n### Anti-Patterns Verdict\n**Start here.** Pass/fail: Does this look AI-generated? List specific tells. Be brutally honest.\n\n### Executive Summary\n- Audit Health Score: **??/20** ([rating band])\n- Total issues found (count by severity: P0/P1/P2/P3)\n- Top 3-5 critical issues\n- Recommended next steps\n\n### Detailed Findings by Severity\n\nTag every issue with **P0-P3 severity**:\n- **P0 Blocking**: Prevents task completion — fix immediately\n- **P1 Major**: Significant difficulty or WCAG AA violation — fix before release\n- **P2 Minor**: Annoyance, workaround exists — fix in next pass\n- **P3 Polish**: Nice-to-fix, no real user impact — fix if time permits\n\nFor each issue, document:\n- **[P?] Issue name**\n- **Location**: Component, file, line\n- **Category**: Accessibility / Performance / Theming / Responsive / Anti-Pattern\n- **Impact**: How it affects users\n- **WCAG/Standard**: Which standard it violates (if applicable)\n- **Recommendation**: How to fix it\n- **Suggested command**: Which command to use (prefer: /animate, /quieter, /optimize, /adapt, /clarify, /distill, /delight, /onboard, /normalize, /audit, /harden, /polish, /extract, /bolder, /arrange, /typeset, /critique, /colorize, /overdrive)\n\n### Patterns & Systemic Issues\n\nIdentify recurring problems that indicate systemic gaps rather than one-off mistakes:\n- \"Hard-coded colors appear in 15+ components, should use design tokens\"\n- \"Touch targets consistently too small (<44px) throughout mobile experience\"\n\n### Positive Findings\n\nNote what's working well — good practices to maintain and replicate.\n\n## Recommended Actions\n\nList recommended commands in priority order (P0 first, then P1, then P2):\n\n1. **[P?] `/command-name`** — Brief description (specific context from audit findings)\n2. **[P?] `/command-name`** — Brief description (specific context)\n\n**Rules**: Only recommend commands from: /animate, /quieter, /optimize, /adapt, /clarify, /distill, /delight, /onboard, /normalize, /audit, /harden, /polish, /extract, /bolder, /arrange, /typeset, /critique, /colorize, /overdrive. Map findings to the most appropriate command. End with `/polish` as the final step if any fixes were recommended.\n\nAfter presenting the summary, tell the user:\n\n> You can ask me to run these one at a time, all at once, or in any order you prefer.\n>\n> Re-run `/audit` after fixes to see your score improve.\n\n**IMPORTANT**: Be thorough but actionable. Too many P3 issues creates noise. Focus on what actually matters.\n\n**NEVER**:\n- Report issues without explaining impact (why does this matter?)\n- Provide generic recommendations (be specific and actionable)\n- Skip positive findings (celebrate what works)\n- Forget to prioritize (everything can't be P0)\n- Report false positives without verification\n\nRemember: You're a technical quality auditor. Document systematically, prioritize ruthlessly, cite specific code locations, and provide clear paths to improvement."}],"isError":false,"timestamp":1775940985954}}
{"type":"message_end","message":{"role":"toolResult","toolCallId":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","toolName":"read","content":[{"type":"text","text":"---\nname: audit\ndescription: Run technical quality checks across accessibility, performance, theming, responsive design, and anti-patterns. Generates a scored report with P0-P3 severity ratings and actionable plan. Use when the user wants an accessibility check, performance audit, or technical quality review.\n---\n\n## MANDATORY PREPARATION\n\nInvoke /frontend-design — it contains design principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first.\n\n---\n\nRun systematic **technical** quality checks and generate a comprehensive report. Don't fix issues — document them for other commands to address.\n\nThis is a code-level audit, not a design critique. Check what's measurable and verifiable in the implementation.\n\n## Diagnostic Scan\n\nRun comprehensive checks across 5 dimensions. Score each dimension 0-4 using the criteria below.\n\n### 1. Accessibility (A11y)\n\n**Check for**:\n- **Contrast issues**: Text contrast ratios < 4.5:1 (or 7:1 for AAA)\n- **Missing ARIA**: Interactive elements without proper roles, labels, or states\n- **Keyboard navigation**: Missing focus indicators, illogical tab order, keyboard traps\n- **Semantic HTML**: Improper heading hierarchy, missing landmarks, divs instead of buttons\n- **Alt text**: Missing or poor image descriptions\n- **Form issues**: Inputs without labels, poor error messaging, missing required indicators\n\n**Score 0-4**: 0=Inaccessible (fails WCAG A), 1=Major gaps (few ARIA labels, no keyboard nav), 2=Partial (some a11y effort, significant gaps), 3=Good (WCAG AA mostly met, minor gaps), 4=Excellent (WCAG AA fully met, approaches AAA)\n\n### 2. Performance\n\n**Check for**:\n- **Layout thrashing**: Reading/writing layout properties in loops\n- **Expensive animations**: Animating layout properties (width, height, top, left) instead of transform/opacity\n- **Missing optimization**: Images without lazy loading, unoptimized assets, missing will-change\n- **Bundle size**: Unnecessary imports, unused dependencies\n- **Render performance**: Unnecessary re-renders, missing memoization\n\n**Score 0-4**: 0=Severe issues (layout thrash, unoptimized everything), 1=Major problems (no lazy loading, expensive animations), 2=Partial (some optimization, gaps remain), 3=Good (mostly optimized, minor improvements possible), 4=Excellent (fast, lean, well-optimized)\n\n### 3. Theming\n\n**Check for**:\n- **Hard-coded colors**: Colors not using design tokens\n- **Broken dark mode**: Missing dark mode variants, poor contrast in dark theme\n- **Inconsistent tokens**: Using wrong tokens, mixing token types\n- **Theme switching issues**: Values that don't update on theme change\n\n**Score 0-4**: 0=No theming (hard-coded everything), 1=Minimal tokens (mostly hard-coded), 2=Partial (tokens exist but inconsistently used), 3=Good (tokens used, minor hard-coded values), 4=Excellent (full token system, dark mode works perfectly)\n\n### 4. Responsive Design\n\n**Check for**:\n- **Fixed widths**: Hard-coded widths that break on mobile\n- **Touch targets**: Interactive elements < 44x44px\n- **Horizontal scroll**: Content overflow on narrow viewports\n- **Text scaling**: Layouts that break when text size increases\n- **Missing breakpoints**: No mobile/tablet variants\n\n**Score 0-4**: 0=Desktop-only (breaks on mobile), 1=Major issues (some breakpoints, many failures), 2=Partial (works on mobile, rough edges), 3=Good (responsive, minor touch target or overflow issues), 4=Excellent (fluid, all viewports, proper touch targets)\n\n### 5. Anti-Patterns (CRITICAL)\n\nCheck against ALL the **DON'T** guidelines in the frontend-design skill. Look for AI slop tells (AI color palette, gradient text, glassmorphism, hero metrics, card grids, generic fonts) and general design anti-patterns (gray on color, nested cards, bounce easing, redundant copy).\n\n**Score 0-4**: 0=AI slop gallery (5+ tells), 1=Heavy AI aesthetic (3-4 tells), 2=Some tells (1-2 noticeable), 3=Mostly clean (subtle issues only), 4=No AI tells (distinctive, intentional design)\n\n## Generate Report\n\n### Audit Health Score\n\n| # | Dimension | Score | Key Finding |\n|---|-----------|-------|-------------|\n| 1 | Accessibility | ? | [most critical a11y issue or \"--\"] |\n| 2 | Performance | ? | |\n| 3 | Responsive Design | ? | |\n| 4 | Theming | ? | |\n| 5 | Anti-Patterns | ? | |\n| **Total** | | **??/20** | **[Rating band]** |\n\n**Rating bands**: 18-20 Excellent (minor polish), 14-17 Good (address weak dimensions), 10-13 Acceptable (significant work needed), 6-9 Poor (major overhaul), 0-5 Critical (fundamental issues)\n\n### Anti-Patterns Verdict\n**Start here.** Pass/fail: Does this look AI-generated? List specific tells. Be brutally honest.\n\n### Executive Summary\n- Audit Health Score: **??/20** ([rating band])\n- Total issues found (count by severity: P0/P1/P2/P3)\n- Top 3-5 critical issues\n- Recommended next steps\n\n### Detailed Findings by Severity\n\nTag every issue with **P0-P3 severity**:\n- **P0 Blocking**: Prevents task completion — fix immediately\n- **P1 Major**: Significant difficulty or WCAG AA violation — fix before release\n- **P2 Minor**: Annoyance, workaround exists — fix in next pass\n- **P3 Polish**: Nice-to-fix, no real user impact — fix if time permits\n\nFor each issue, document:\n- **[P?] Issue name**\n- **Location**: Component, file, line\n- **Category**: Accessibility / Performance / Theming / Responsive / Anti-Pattern\n- **Impact**: How it affects users\n- **WCAG/Standard**: Which standard it violates (if applicable)\n- **Recommendation**: How to fix it\n- **Suggested command**: Which command to use (prefer: /animate, /quieter, /optimize, /adapt, /clarify, /distill, /delight, /onboard, /normalize, /audit, /harden, /polish, /extract, /bolder, /arrange, /typeset, /critique, /colorize, /overdrive)\n\n### Patterns & Systemic Issues\n\nIdentify recurring problems that indicate systemic gaps rather than one-off mistakes:\n- \"Hard-coded colors appear in 15+ components, should use design tokens\"\n- \"Touch targets consistently too small (<44px) throughout mobile experience\"\n\n### Positive Findings\n\nNote what's working well — good practices to maintain and replicate.\n\n## Recommended Actions\n\nList recommended commands in priority order (P0 first, then P1, then P2):\n\n1. **[P?] `/command-name`** — Brief description (specific context from audit findings)\n2. **[P?] `/command-name`** — Brief description (specific context)\n\n**Rules**: Only recommend commands from: /animate, /quieter, /optimize, /adapt, /clarify, /distill, /delight, /onboard, /normalize, /audit, /harden, /polish, /extract, /bolder, /arrange, /typeset, /critique, /colorize, /overdrive. Map findings to the most appropriate command. End with `/polish` as the final step if any fixes were recommended.\n\nAfter presenting the summary, tell the user:\n\n> You can ask me to run these one at a time, all at once, or in any order you prefer.\n>\n> Re-run `/audit` after fixes to see your score improve.\n\n**IMPORTANT**: Be thorough but actionable. Too many P3 issues creates noise. Focus on what actually matters.\n\n**NEVER**:\n- Report issues without explaining impact (why does this matter?)\n- Provide generic recommendations (be specific and actionable)\n- Skip positive findings (celebrate what works)\n- Forget to prioritize (everything can't be P0)\n- Report false positives without verification\n\nRemember: You're a technical quality auditor. Document systematically, prioritize ruthlessly, cite specific code locations, and provide clear paths to improvement."}],"isError":false,"timestamp":1775940985954}}
{"type":"turn_end","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":484,"output":962,"cacheRead":2560,"cacheWrite":0,"totalTokens":4006,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775940974429,"responseId":"ne8qzSbGOSg4QPKPAMgRtHxbYLUNtGitqhqLzZz0CG6IO5y2TWDwBM0AZnY0iUal1NNkCwx7DtdEu0Q2k4mCfrpPv1YlSm4UbrxdJHT7iXPbUiqAhB7pAUwIl6+hyb0cRxnyH8IP3ns7B5gjWChkdUqavcREgklue+pqo+vKFJkIPus8w8A+VFAKh/EydmsuVgrjG8pAmWFsX9FX0rgqwk7zniYOmGknmG3+60dpCVQxXB6HzBTdWXRbNMPpozHtEFdyWDa0Bx19yEmXDghO/cRro7ZkcgKIHfsHki7JIpFlVyyLX/2UGpVcRPQWaqeRIeEAzjtCGz3zwQBEJxK6kVxKM2TNRJKQd+ZmvZnyNUGsNUq7NQ+tRJK9ytJb0TiJwUbkmZ3W2u+qSJAiwiBowlyFcCc6"},"toolResults":[{"role":"toolResult","toolCallId":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","toolName":"read","content":[{"type":"text","text":"---\nname: audit\ndescription: Run technical quality checks across accessibility, performance, theming, responsive design, and anti-patterns. Generates a scored report with P0-P3 severity ratings and actionable plan. Use when the user wants an accessibility check, performance audit, or technical quality review.\n---\n\n## MANDATORY PREPARATION\n\nInvoke /frontend-design — it contains design principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first.\n\n---\n\nRun systematic **technical** quality checks and generate a comprehensive report. Don't fix issues — document them for other commands to address.\n\nThis is a code-level audit, not a design critique. Check what's measurable and verifiable in the implementation.\n\n## Diagnostic Scan\n\nRun comprehensive checks across 5 dimensions. Score each dimension 0-4 using the criteria below.\n\n### 1. Accessibility (A11y)\n\n**Check for**:\n- **Contrast issues**: Text contrast ratios < 4.5:1 (or 7:1 for AAA)\n- **Missing ARIA**: Interactive elements without proper roles, labels, or states\n- **Keyboard navigation**: Missing focus indicators, illogical tab order, keyboard traps\n- **Semantic HTML**: Improper heading hierarchy, missing landmarks, divs instead of buttons\n- **Alt text**: Missing or poor image descriptions\n- **Form issues**: Inputs without labels, poor error messaging, missing required indicators\n\n**Score 0-4**: 0=Inaccessible (fails WCAG A), 1=Major gaps (few ARIA labels, no keyboard nav), 2=Partial (some a11y effort, significant gaps), 3=Good (WCAG AA mostly met, minor gaps), 4=Excellent (WCAG AA fully met, approaches AAA)\n\n### 2. Performance\n\n**Check for**:\n- **Layout thrashing**: Reading/writing layout properties in loops\n- **Expensive animations**: Animating layout properties (width, height, top, left) instead of transform/opacity\n- **Missing optimization**: Images without lazy loading, unoptimized assets, missing will-change\n- **Bundle size**: Unnecessary imports, unused dependencies\n- **Render performance**: Unnecessary re-renders, missing memoization\n\n**Score 0-4**: 0=Severe issues (layout thrash, unoptimized everything), 1=Major problems (no lazy loading, expensive animations), 2=Partial (some optimization, gaps remain), 3=Good (mostly optimized, minor improvements possible), 4=Excellent (fast, lean, well-optimized)\n\n### 3. Theming\n\n**Check for**:\n- **Hard-coded colors**: Colors not using design tokens\n- **Broken dark mode**: Missing dark mode variants, poor contrast in dark theme\n- **Inconsistent tokens**: Using wrong tokens, mixing token types\n- **Theme switching issues**: Values that don't update on theme change\n\n**Score 0-4**: 0=No theming (hard-coded everything), 1=Minimal tokens (mostly hard-coded), 2=Partial (tokens exist but inconsistently used), 3=Good (tokens used, minor hard-coded values), 4=Excellent (full token system, dark mode works perfectly)\n\n### 4. Responsive Design\n\n**Check for**:\n- **Fixed widths**: Hard-coded widths that break on mobile\n- **Touch targets**: Interactive elements < 44x44px\n- **Horizontal scroll**: Content overflow on narrow viewports\n- **Text scaling**: Layouts that break when text size increases\n- **Missing breakpoints**: No mobile/tablet variants\n\n**Score 0-4**: 0=Desktop-only (breaks on mobile), 1=Major issues (some breakpoints, many failures), 2=Partial (works on mobile, rough edges), 3=Good (responsive, minor touch target or overflow issues), 4=Excellent (fluid, all viewports, proper touch targets)\n\n### 5. Anti-Patterns (CRITICAL)\n\nCheck against ALL the **DON'T** guidelines in the frontend-design skill. Look for AI slop tells (AI color palette, gradient text, glassmorphism, hero metrics, card grids, generic fonts) and general design anti-patterns (gray on color, nested cards, bounce easing, redundant copy).\n\n**Score 0-4**: 0=AI slop gallery (5+ tells), 1=Heavy AI aesthetic (3-4 tells), 2=Some tells (1-2 noticeable), 3=Mostly clean (subtle issues only), 4=No AI tells (distinctive, intentional design)\n\n## Generate Report\n\n### Audit Health Score\n\n| # | Dimension | Score | Key Finding |\n|---|-----------|-------|-------------|\n| 1 | Accessibility | ? | [most critical a11y issue or \"--\"] |\n| 2 | Performance | ? | |\n| 3 | Responsive Design | ? | |\n| 4 | Theming | ? | |\n| 5 | Anti-Patterns | ? | |\n| **Total** | | **??/20** | **[Rating band]** |\n\n**Rating bands**: 18-20 Excellent (minor polish), 14-17 Good (address weak dimensions), 10-13 Acceptable (significant work needed), 6-9 Poor (major overhaul), 0-5 Critical (fundamental issues)\n\n### Anti-Patterns Verdict\n**Start here.** Pass/fail: Does this look AI-generated? List specific tells. Be brutally honest.\n\n### Executive Summary\n- Audit Health Score: **??/20** ([rating band])\n- Total issues found (count by severity: P0/P1/P2/P3)\n- Top 3-5 critical issues\n- Recommended next steps\n\n### Detailed Findings by Severity\n\nTag every issue with **P0-P3 severity**:\n- **P0 Blocking**: Prevents task completion — fix immediately\n- **P1 Major**: Significant difficulty or WCAG AA violation — fix before release\n- **P2 Minor**: Annoyance, workaround exists — fix in next pass\n- **P3 Polish**: Nice-to-fix, no real user impact — fix if time permits\n\nFor each issue, document:\n- **[P?] Issue name**\n- **Location**: Component, file, line\n- **Category**: Accessibility / Performance / Theming / Responsive / Anti-Pattern\n- **Impact**: How it affects users\n- **WCAG/Standard**: Which standard it violates (if applicable)\n- **Recommendation**: How to fix it\n- **Suggested command**: Which command to use (prefer: /animate, /quieter, /optimize, /adapt, /clarify, /distill, /delight, /onboard, /normalize, /audit, /harden, /polish, /extract, /bolder, /arrange, /typeset, /critique, /colorize, /overdrive)\n\n### Patterns & Systemic Issues\n\nIdentify recurring problems that indicate systemic gaps rather than one-off mistakes:\n- \"Hard-coded colors appear in 15+ components, should use design tokens\"\n- \"Touch targets consistently too small (<44px) throughout mobile experience\"\n\n### Positive Findings\n\nNote what's working well — good practices to maintain and replicate.\n\n## Recommended Actions\n\nList recommended commands in priority order (P0 first, then P1, then P2):\n\n1. **[P?] `/command-name`** — Brief description (specific context from audit findings)\n2. **[P?] `/command-name`** — Brief description (specific context)\n\n**Rules**: Only recommend commands from: /animate, /quieter, /optimize, /adapt, /clarify, /distill, /delight, /onboard, /normalize, /audit, /harden, /polish, /extract, /bolder, /arrange, /typeset, /critique, /colorize, /overdrive. Map findings to the most appropriate command. End with `/polish` as the final step if any fixes were recommended.\n\nAfter presenting the summary, tell the user:\n\n> You can ask me to run these one at a time, all at once, or in any order you prefer.\n>\n> Re-run `/audit` after fixes to see your score improve.\n\n**IMPORTANT**: Be thorough but actionable. Too many P3 issues creates noise. Focus on what actually matters.\n\n**NEVER**:\n- Report issues without explaining impact (why does this matter?)\n- Provide generic recommendations (be specific and actionable)\n- Skip positive findings (celebrate what works)\n- Forget to prioritize (everything can't be P0)\n- Report false positives without verification\n\nRemember: You're a technical quality auditor. Document systematically, prioritize ruthlessly, cite specific code locations, and provide clear paths to improvement."}],"isError":false,"timestamp":1775940985954}]}
{"type":"turn_start"}
{"type":"message_start","message":{"role":"assistant","content":[],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_start","contentIndex":0,"partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{},"partialJson":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{},"partialJson":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"{\"","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"path","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{},"partialJson":"{\"path"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{},"partialJson":"{\"path"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"\":","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{},"partialJson":"{\"path\":"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{},"partialJson":"{\"path\":"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"\"/","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/"},"partialJson":"{\"path\":\"/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/"},"partialJson":"{\"path\":\"/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"home","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home"},"partialJson":"{\"path\":\"/home"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home"},"partialJson":"{\"path\":\"/home"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/al","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/al"},"partialJson":"{\"path\":\"/home/al"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/al"},"partialJson":"{\"path\":\"/home/al"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"ex","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex"},"partialJson":"{\"path\":\"/home/alex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex"},"partialJson":"{\"path\":\"/home/alex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/.","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/."},"partialJson":"{\"path\":\"/home/alex/."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/."},"partialJson":"{\"path\":\"/home/alex/."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"pi","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi"},"partialJson":"{\"path\":\"/home/alex/.pi"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi"},"partialJson":"{\"path\":\"/home/alex/.pi"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/"},"partialJson":"{\"path\":\"/home/alex/.pi/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/"},"partialJson":"{\"path\":\"/home/alex/.pi/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"agent","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent"},"partialJson":"{\"path\":\"/home/alex/.pi/agent"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent"},"partialJson":"{\"path\":\"/home/alex/.pi/agent"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"skills","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/frontend","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/frontend","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/S","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design/S"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend-design/S"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design/S"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend-design/S"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"K","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design/SK"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend-design/SK"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design/SK"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend-design/SK"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"ILL","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":".md","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"\"}","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_end","contentIndex":0,"toolCall":{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|NUHLZQFoYkJU6tJRsCrxu5t1nv7NL2kqJ7D4nkNmNWgWgQjz26Eks0FBfDmqAnQpr45g5h1aVp3Z+S5b9NE+I5YdziuOsij49yKTx+Axa6A7H2k6dvc5DKgZKV55p0xWVXbyCg7Q15sNee238GKO+1O5yE18+d8fE//AgpEWagmId7gI15e23oZsK0dAPRqRKlqRblzEYf3NrGNORmS57wetjtCslJ2JuUFR2sWU3QMCH0jmYi/a5gmiRyDLxtYHTboCWHocmjF03N/zTe4uenp6DaL2xeQyWCju+rNAv3lvrU8KOk50+v6npJyP4Ro1Ce1NMtbqzgdaPD2u2GYBBn0itmmHVNRhD81NrTPAaeRc8Va+V3VnkrNdFxD96kRu5f1H/I5q+jfNp6mDhKMvAVrkdn0F214=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md"}},"partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940985960,"responseId":"9Vj/ULcZEBnvfujmj1bhveymCrYCgY64KpcSphCJWLhzUenQxTMQnNUrX+NV1S2FkkJ+m7HE/9Rxc9BIiLgSjYMvOF9Ppx0HZnvMdCAQtenJg1bwd/Igiz8kj3x0KpwqqkG9DNWBkbZRiwchFQRRa3Bh9W6kfsOp+qJAi/v0DbbI0pUlifRdhIrynXcjLpotIV2/8g3n83hbHeOtGaDlhII6Fkn0XX1FXoZiSk5wHn3QeaW6A8YBFjmBr4i6IhqCOZZ+gdiIOzJfrcMfJ3LBObh1NZoRPh1a5eT+F0LqWtdDQplgKGUXPuYNgHMFApVFXqznU5a0aRckpQtRzCSpQJw5MO8o2JUwtdPtZzZew2KRSmhpFbgi54W1SYdoSHE0j6mJ8hPvOkaMp6w2CWPnV0QYslKjoR54wQ=="}}
{"type":"message_end","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":2584,"output":33,"cacheRead":2560,"cacheWrite":0,"totalTokens":5177,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775940985960,"responseId":"FNBTthE06M2/fLDOM/K894FBfAdQAVFhtf2TPOs9HNd4rR3159o2tLEuwuTiDRMfDsZ7w4YKU9RkkcwH8vgq1urXJaYIg3dir0SURvSrtn9Taoto7iVWTscFZNMW/qPP4QBIAmOpKIijbUNag6irlWV/xf2guGJ4ccOLbK/qJuI2MSszHoi0SeXVf7hJNooRuJps8kHtCuqRngxMtYGtXMwnjL5JkV18E+W71TwabHWtAl7+sNOJKNHZnLpfmgvIbKKqrEz30jI1inaBnGTAUInI1h3dor2nTll2IIeWItR9eJ+Za+F/4WeBo1qBCoEJggBqzzYxhjkdHrHkqbyULn8BtE/AA8cMCQTjQoOOPBLWz2ysz5YMMrToU8e7TC/VeVRlNTWE2BWGRMiXdM2rhRP8FMFBTQnd6A=="}}
{"type":"tool_execution_start","toolCallId":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","toolName":"read","args":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md"}}
{"type":"tool_execution_end","toolCallId":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","toolName":"read","result":{"content":[{"type":"text","text":"---\nname: frontend-design\ndescription: Create distinctive, production-grade frontend interfaces with high design quality. Generates creative, polished code that avoids generic AI aesthetics. Use when the user asks to build web components, pages, artifacts, posters, or applications, or when any design skill requires project context.\nlicense: Apache 2.0. Based on Anthropic's frontend-design skill. See NOTICE.md for attribution.\n---\n\nThis skill guides creation of distinctive, production-grade frontend interfaces that avoid generic \"AI slop\" aesthetics. Implement real working code with exceptional attention to aesthetic details and creative choices.\n\n## Context Gathering Protocol\n\nDesign skills produce generic output without project context. You MUST have confirmed design context before doing any design work.\n\n**Required context** — every design skill needs at minimum:\n- **Target audience**: Who uses this product and in what context?\n- **Use cases**: What jobs are they trying to get done?\n- **Brand personality/tone**: How should the interface feel?\n\nIndividual skills may require additional context — check the skill's preparation section for specifics.\n\n**CRITICAL**: You cannot infer this context by reading the codebase. Code tells you what was built, not who it's for or what it should feel like. Only the creator can provide this context.\n\n**Gathering order:**\n1. **Check current instructions (instant)**: If your loaded instructions already contain a **Design Context** section, proceed immediately.\n2. **Check .impeccable.md (fast)**: If not in instructions, read `.impeccable.md` from the project root. If it exists and contains the required context, proceed.\n3. **Run teach-impeccable (REQUIRED)**: If neither source has context, you MUST run /teach-impeccable NOW before doing anything else. Do NOT skip this step. Do NOT attempt to infer context from the codebase instead.\n\n---\n\n## Design Direction\n\nCommit to a BOLD aesthetic direction:\n- **Purpose**: What problem does this interface solve? Who uses it?\n- **Tone**: Pick an extreme: brutally minimal, maximalist chaos, retro-futuristic, organic/natural, luxury/refined, playful/toy-like, editorial/magazine, brutalist/raw, art deco/geometric, soft/pastel, industrial/utilitarian, etc. There are so many flavors to choose from. Use these for inspiration but design one that is true to the aesthetic direction.\n- **Constraints**: Technical requirements (framework, performance, accessibility).\n- **Differentiation**: What makes this UNFORGETTABLE? What's the one thing someone will remember?\n\n**CRITICAL**: Choose a clear conceptual direction and execute it with precision. Bold maximalism and refined minimalism both work—the key is intentionality, not intensity.\n\nThen implement working code that is:\n- Production-grade and functional\n- Visually striking and memorable\n- Cohesive with a clear aesthetic point-of-view\n- Meticulously refined in every detail\n\n## Frontend Aesthetics Guidelines\n\n### Typography\n→ *Consult [typography reference](reference/typography.md) for scales, pairing, and loading strategies.*\n\nChoose fonts that are beautiful, unique, and interesting. Pair a distinctive display font with a refined body font.\n\n**DO**: Use a modular type scale with fluid sizing (clamp)\n**DO**: Vary font weights and sizes to create clear visual hierarchy\n**DON'T**: Use overused fonts—Inter, Roboto, Arial, Open Sans, system defaults\n**DON'T**: Use monospace typography as lazy shorthand for \"technical/developer\" vibes\n**DON'T**: Put large icons with rounded corners above every heading—they rarely add value and make sites look templated\n\n### Color & Theme\n→ *Consult [color reference](reference/color-and-contrast.md) for OKLCH, palettes, and dark mode.*\n\nCommit to a cohesive palette. Dominant colors with sharp accents outperform timid, evenly-distributed palettes.\n\n**DO**: Use modern CSS color functions (oklch, color-mix, light-dark) for perceptually uniform, maintainable palettes\n**DO**: Tint your neutrals toward your brand hue—even a subtle hint creates subconscious cohesion\n**DON'T**: Use gray text on colored backgrounds—it looks washed out; use a shade of the background color instead\n**DON'T**: Use pure black (#000) or pure white (#fff)—always tint; pure black/white never appears in nature\n**DON'T**: Use the AI color palette: cyan-on-dark, purple-to-blue gradients, neon accents on dark backgrounds\n**DON'T**: Use gradient text for \"impact\"—especially on metrics or headings; it's decorative rather than meaningful\n**DON'T**: Default to dark mode with glowing accents—it looks \"cool\" without requiring actual design decisions\n\n### Layout & Space\n→ *Consult [spatial reference](reference/spatial-design.md) for grids, rhythm, and container queries.*\n\nCreate visual rhythm through varied spacing—not the same padding everywhere. Embrace asymmetry and unexpected compositions. Break the grid intentionally for emphasis.\n\n**DO**: Create visual rhythm through varied spacing—tight groupings, generous separations\n**DO**: Use fluid spacing with clamp() that breathes on larger screens\n**DO**: Use asymmetry and unexpected compositions; break the grid intentionally for emphasis\n**DON'T**: Wrap everything in cards—not everything needs a container\n**DON'T**: Nest cards inside cards—visual noise, flatten the hierarchy\n**DON'T**: Use identical card grids—same-sized cards with icon + heading + text, repeated endlessly\n**DON'T**: Use the hero metric layout template—big number, small label, supporting stats, gradient accent\n**DON'T**: Center everything—left-aligned text with asymmetric layouts feels more designed\n**DON'T**: Use the same spacing everywhere—without rhythm, layouts feel monotonous\n\n### Visual Details\n**DO**: Use intentional, purposeful decorative elements that reinforce brand\n**DON'T**: Use glassmorphism everywhere—blur effects, glass cards, glow borders used decoratively rather than purposefully\n**DON'T**: Use rounded elements with thick colored border on one side—a lazy accent that almost never looks intentional\n**DON'T**: Use sparklines as decoration—tiny charts that look sophisticated but convey nothing meaningful\n**DON'T**: Use rounded rectangles with generic drop shadows—safe, forgettable, could be any AI output\n**DON'T**: Use modals unless there's truly no better alternative—modals are lazy\n\n### Motion\n→ *Consult [motion reference](reference/motion-design.md) for timing, easing, and reduced motion.*\n\nFocus on high-impact moments: one well-orchestrated page load with staggered reveals creates more delight than scattered micro-interactions.\n\n**DO**: Use motion to convey state changes—entrances, exits, feedback\n**DO**: Use exponential easing (ease-out-quart/quint/expo) for natural deceleration\n**DO**: For height animations, use grid-template-rows transitions instead of animating height directly\n**DON'T**: Animate layout properties (width, height, padding, margin)—use transform and opacity only\n**DON'T**: Use bounce or elastic easing—they feel dated and tacky; real objects decelerate smoothly\n\n### Interaction\n→ *Consult [interaction reference](reference/interaction-design.md) for forms, focus, and loading patterns.*\n\nMake interactions feel fast. Use optimistic UI—update immediately, sync later.\n\n**DO**: Use progressive disclosure—start simple, reveal sophistication through interaction (basic options first, advanced behind expandable sections; hover states that reveal secondary actions)\n**DO**: Design empty states that teach the interface, not just say \"nothing here\"\n**DO**: Make every interactive surface feel intentional and responsive\n**DON'T**: Repeat the same information—redundant headers, intros that restate the heading\n**DON'T**: Make every button primary—use ghost buttons, text links, secondary styles; hierarchy matters\n\n### Responsive\n→ *Consult [responsive reference](reference/responsive-design.md) for mobile-first, fluid design, and container queries.*\n\n**DO**: Use container queries (@container) for component-level responsiveness\n**DO**: Adapt the interface for different contexts—don't just shrink it\n**DON'T**: Hide critical functionality on mobile—adapt the interface, don't amputate it\n\n### UX Writing\n→ *Consult [ux-writing reference](reference/ux-writing.md) for labels, errors, and empty states.*\n\n**DO**: Make every word earn its place\n**DON'T**: Repeat information users can already see\n\n---\n\n## The AI Slop Test\n\n**Critical quality check**: If you showed this interface to someone and said \"AI made this,\" would they believe you immediately? If yes, that's the problem.\n\nA distinctive interface should make someone ask \"how was this made?\" not \"which AI made this?\"\n\nReview the DON'T guidelines above—they are the fingerprints of AI-generated work from 2024-2025.\n\n---\n\n## Implementation Principles\n\nMatch implementation complexity to the aesthetic vision. Maximalist designs need elaborate code with extensive animations and effects. Minimalist or refined designs need restraint, precision, and careful attention to spacing, typography, and subtle details.\n\nInterpret creatively and make unexpected choices that feel genuinely designed for the context. No design should be the same. Vary between light and dark themes, different fonts, different aesthetics. NEVER converge on common choices across generations.\n\nRemember: the model is capable of extraordinary creative work. Don't hold back—show what can truly be created when thinking outside the box and committing fully to a distinctive vision."}]},"isError":false}
{"type":"message_start","message":{"role":"toolResult","toolCallId":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","toolName":"read","content":[{"type":"text","text":"---\nname: frontend-design\ndescription: Create distinctive, production-grade frontend interfaces with high design quality. Generates creative, polished code that avoids generic AI aesthetics. Use when the user asks to build web components, pages, artifacts, posters, or applications, or when any design skill requires project context.\nlicense: Apache 2.0. Based on Anthropic's frontend-design skill. See NOTICE.md for attribution.\n---\n\nThis skill guides creation of distinctive, production-grade frontend interfaces that avoid generic \"AI slop\" aesthetics. Implement real working code with exceptional attention to aesthetic details and creative choices.\n\n## Context Gathering Protocol\n\nDesign skills produce generic output without project context. You MUST have confirmed design context before doing any design work.\n\n**Required context** — every design skill needs at minimum:\n- **Target audience**: Who uses this product and in what context?\n- **Use cases**: What jobs are they trying to get done?\n- **Brand personality/tone**: How should the interface feel?\n\nIndividual skills may require additional context — check the skill's preparation section for specifics.\n\n**CRITICAL**: You cannot infer this context by reading the codebase. Code tells you what was built, not who it's for or what it should feel like. Only the creator can provide this context.\n\n**Gathering order:**\n1. **Check current instructions (instant)**: If your loaded instructions already contain a **Design Context** section, proceed immediately.\n2. **Check .impeccable.md (fast)**: If not in instructions, read `.impeccable.md` from the project root. If it exists and contains the required context, proceed.\n3. **Run teach-impeccable (REQUIRED)**: If neither source has context, you MUST run /teach-impeccable NOW before doing anything else. Do NOT skip this step. Do NOT attempt to infer context from the codebase instead.\n\n---\n\n## Design Direction\n\nCommit to a BOLD aesthetic direction:\n- **Purpose**: What problem does this interface solve? Who uses it?\n- **Tone**: Pick an extreme: brutally minimal, maximalist chaos, retro-futuristic, organic/natural, luxury/refined, playful/toy-like, editorial/magazine, brutalist/raw, art deco/geometric, soft/pastel, industrial/utilitarian, etc. There are so many flavors to choose from. Use these for inspiration but design one that is true to the aesthetic direction.\n- **Constraints**: Technical requirements (framework, performance, accessibility).\n- **Differentiation**: What makes this UNFORGETTABLE? What's the one thing someone will remember?\n\n**CRITICAL**: Choose a clear conceptual direction and execute it with precision. Bold maximalism and refined minimalism both work—the key is intentionality, not intensity.\n\nThen implement working code that is:\n- Production-grade and functional\n- Visually striking and memorable\n- Cohesive with a clear aesthetic point-of-view\n- Meticulously refined in every detail\n\n## Frontend Aesthetics Guidelines\n\n### Typography\n→ *Consult [typography reference](reference/typography.md) for scales, pairing, and loading strategies.*\n\nChoose fonts that are beautiful, unique, and interesting. Pair a distinctive display font with a refined body font.\n\n**DO**: Use a modular type scale with fluid sizing (clamp)\n**DO**: Vary font weights and sizes to create clear visual hierarchy\n**DON'T**: Use overused fonts—Inter, Roboto, Arial, Open Sans, system defaults\n**DON'T**: Use monospace typography as lazy shorthand for \"technical/developer\" vibes\n**DON'T**: Put large icons with rounded corners above every heading—they rarely add value and make sites look templated\n\n### Color & Theme\n→ *Consult [color reference](reference/color-and-contrast.md) for OKLCH, palettes, and dark mode.*\n\nCommit to a cohesive palette. Dominant colors with sharp accents outperform timid, evenly-distributed palettes.\n\n**DO**: Use modern CSS color functions (oklch, color-mix, light-dark) for perceptually uniform, maintainable palettes\n**DO**: Tint your neutrals toward your brand hue—even a subtle hint creates subconscious cohesion\n**DON'T**: Use gray text on colored backgrounds—it looks washed out; use a shade of the background color instead\n**DON'T**: Use pure black (#000) or pure white (#fff)—always tint; pure black/white never appears in nature\n**DON'T**: Use the AI color palette: cyan-on-dark, purple-to-blue gradients, neon accents on dark backgrounds\n**DON'T**: Use gradient text for \"impact\"—especially on metrics or headings; it's decorative rather than meaningful\n**DON'T**: Default to dark mode with glowing accents—it looks \"cool\" without requiring actual design decisions\n\n### Layout & Space\n→ *Consult [spatial reference](reference/spatial-design.md) for grids, rhythm, and container queries.*\n\nCreate visual rhythm through varied spacing—not the same padding everywhere. Embrace asymmetry and unexpected compositions. Break the grid intentionally for emphasis.\n\n**DO**: Create visual rhythm through varied spacing—tight groupings, generous separations\n**DO**: Use fluid spacing with clamp() that breathes on larger screens\n**DO**: Use asymmetry and unexpected compositions; break the grid intentionally for emphasis\n**DON'T**: Wrap everything in cards—not everything needs a container\n**DON'T**: Nest cards inside cards—visual noise, flatten the hierarchy\n**DON'T**: Use identical card grids—same-sized cards with icon + heading + text, repeated endlessly\n**DON'T**: Use the hero metric layout template—big number, small label, supporting stats, gradient accent\n**DON'T**: Center everything—left-aligned text with asymmetric layouts feels more designed\n**DON'T**: Use the same spacing everywhere—without rhythm, layouts feel monotonous\n\n### Visual Details\n**DO**: Use intentional, purposeful decorative elements that reinforce brand\n**DON'T**: Use glassmorphism everywhere—blur effects, glass cards, glow borders used decoratively rather than purposefully\n**DON'T**: Use rounded elements with thick colored border on one side—a lazy accent that almost never looks intentional\n**DON'T**: Use sparklines as decoration—tiny charts that look sophisticated but convey nothing meaningful\n**DON'T**: Use rounded rectangles with generic drop shadows—safe, forgettable, could be any AI output\n**DON'T**: Use modals unless there's truly no better alternative—modals are lazy\n\n### Motion\n→ *Consult [motion reference](reference/motion-design.md) for timing, easing, and reduced motion.*\n\nFocus on high-impact moments: one well-orchestrated page load with staggered reveals creates more delight than scattered micro-interactions.\n\n**DO**: Use motion to convey state changes—entrances, exits, feedback\n**DO**: Use exponential easing (ease-out-quart/quint/expo) for natural deceleration\n**DO**: For height animations, use grid-template-rows transitions instead of animating height directly\n**DON'T**: Animate layout properties (width, height, padding, margin)—use transform and opacity only\n**DON'T**: Use bounce or elastic easing—they feel dated and tacky; real objects decelerate smoothly\n\n### Interaction\n→ *Consult [interaction reference](reference/interaction-design.md) for forms, focus, and loading patterns.*\n\nMake interactions feel fast. Use optimistic UI—update immediately, sync later.\n\n**DO**: Use progressive disclosure—start simple, reveal sophistication through interaction (basic options first, advanced behind expandable sections; hover states that reveal secondary actions)\n**DO**: Design empty states that teach the interface, not just say \"nothing here\"\n**DO**: Make every interactive surface feel intentional and responsive\n**DON'T**: Repeat the same information—redundant headers, intros that restate the heading\n**DON'T**: Make every button primary—use ghost buttons, text links, secondary styles; hierarchy matters\n\n### Responsive\n→ *Consult [responsive reference](reference/responsive-design.md) for mobile-first, fluid design, and container queries.*\n\n**DO**: Use container queries (@container) for component-level responsiveness\n**DO**: Adapt the interface for different contexts—don't just shrink it\n**DON'T**: Hide critical functionality on mobile—adapt the interface, don't amputate it\n\n### UX Writing\n→ *Consult [ux-writing reference](reference/ux-writing.md) for labels, errors, and empty states.*\n\n**DO**: Make every word earn its place\n**DON'T**: Repeat information users can already see\n\n---\n\n## The AI Slop Test\n\n**Critical quality check**: If you showed this interface to someone and said \"AI made this,\" would they believe you immediately? If yes, that's the problem.\n\nA distinctive interface should make someone ask \"how was this made?\" not \"which AI made this?\"\n\nReview the DON'T guidelines above—they are the fingerprints of AI-generated work from 2024-2025.\n\n---\n\n## Implementation Principles\n\nMatch implementation complexity to the aesthetic vision. Maximalist designs need elaborate code with extensive animations and effects. Minimalist or refined designs need restraint, precision, and careful attention to spacing, typography, and subtle details.\n\nInterpret creatively and make unexpected choices that feel genuinely designed for the context. No design should be the same. Vary between light and dark themes, different fonts, different aesthetics. NEVER converge on common choices across generations.\n\nRemember: the model is capable of extraordinary creative work. Don't hold back—show what can truly be created when thinking outside the box and committing fully to a distinctive vision."}],"isError":false,"timestamp":1775940988160}}
{"type":"message_end","message":{"role":"toolResult","toolCallId":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","toolName":"read","content":[{"type":"text","text":"---\nname: frontend-design\ndescription: Create distinctive, production-grade frontend interfaces with high design quality. Generates creative, polished code that avoids generic AI aesthetics. Use when the user asks to build web components, pages, artifacts, posters, or applications, or when any design skill requires project context.\nlicense: Apache 2.0. Based on Anthropic's frontend-design skill. See NOTICE.md for attribution.\n---\n\nThis skill guides creation of distinctive, production-grade frontend interfaces that avoid generic \"AI slop\" aesthetics. Implement real working code with exceptional attention to aesthetic details and creative choices.\n\n## Context Gathering Protocol\n\nDesign skills produce generic output without project context. You MUST have confirmed design context before doing any design work.\n\n**Required context** — every design skill needs at minimum:\n- **Target audience**: Who uses this product and in what context?\n- **Use cases**: What jobs are they trying to get done?\n- **Brand personality/tone**: How should the interface feel?\n\nIndividual skills may require additional context — check the skill's preparation section for specifics.\n\n**CRITICAL**: You cannot infer this context by reading the codebase. Code tells you what was built, not who it's for or what it should feel like. Only the creator can provide this context.\n\n**Gathering order:**\n1. **Check current instructions (instant)**: If your loaded instructions already contain a **Design Context** section, proceed immediately.\n2. **Check .impeccable.md (fast)**: If not in instructions, read `.impeccable.md` from the project root. If it exists and contains the required context, proceed.\n3. **Run teach-impeccable (REQUIRED)**: If neither source has context, you MUST run /teach-impeccable NOW before doing anything else. Do NOT skip this step. Do NOT attempt to infer context from the codebase instead.\n\n---\n\n## Design Direction\n\nCommit to a BOLD aesthetic direction:\n- **Purpose**: What problem does this interface solve? Who uses it?\n- **Tone**: Pick an extreme: brutally minimal, maximalist chaos, retro-futuristic, organic/natural, luxury/refined, playful/toy-like, editorial/magazine, brutalist/raw, art deco/geometric, soft/pastel, industrial/utilitarian, etc. There are so many flavors to choose from. Use these for inspiration but design one that is true to the aesthetic direction.\n- **Constraints**: Technical requirements (framework, performance, accessibility).\n- **Differentiation**: What makes this UNFORGETTABLE? What's the one thing someone will remember?\n\n**CRITICAL**: Choose a clear conceptual direction and execute it with precision. Bold maximalism and refined minimalism both work—the key is intentionality, not intensity.\n\nThen implement working code that is:\n- Production-grade and functional\n- Visually striking and memorable\n- Cohesive with a clear aesthetic point-of-view\n- Meticulously refined in every detail\n\n## Frontend Aesthetics Guidelines\n\n### Typography\n→ *Consult [typography reference](reference/typography.md) for scales, pairing, and loading strategies.*\n\nChoose fonts that are beautiful, unique, and interesting. Pair a distinctive display font with a refined body font.\n\n**DO**: Use a modular type scale with fluid sizing (clamp)\n**DO**: Vary font weights and sizes to create clear visual hierarchy\n**DON'T**: Use overused fonts—Inter, Roboto, Arial, Open Sans, system defaults\n**DON'T**: Use monospace typography as lazy shorthand for \"technical/developer\" vibes\n**DON'T**: Put large icons with rounded corners above every heading—they rarely add value and make sites look templated\n\n### Color & Theme\n→ *Consult [color reference](reference/color-and-contrast.md) for OKLCH, palettes, and dark mode.*\n\nCommit to a cohesive palette. Dominant colors with sharp accents outperform timid, evenly-distributed palettes.\n\n**DO**: Use modern CSS color functions (oklch, color-mix, light-dark) for perceptually uniform, maintainable palettes\n**DO**: Tint your neutrals toward your brand hue—even a subtle hint creates subconscious cohesion\n**DON'T**: Use gray text on colored backgrounds—it looks washed out; use a shade of the background color instead\n**DON'T**: Use pure black (#000) or pure white (#fff)—always tint; pure black/white never appears in nature\n**DON'T**: Use the AI color palette: cyan-on-dark, purple-to-blue gradients, neon accents on dark backgrounds\n**DON'T**: Use gradient text for \"impact\"—especially on metrics or headings; it's decorative rather than meaningful\n**DON'T**: Default to dark mode with glowing accents—it looks \"cool\" without requiring actual design decisions\n\n### Layout & Space\n→ *Consult [spatial reference](reference/spatial-design.md) for grids, rhythm, and container queries.*\n\nCreate visual rhythm through varied spacing—not the same padding everywhere. Embrace asymmetry and unexpected compositions. Break the grid intentionally for emphasis.\n\n**DO**: Create visual rhythm through varied spacing—tight groupings, generous separations\n**DO**: Use fluid spacing with clamp() that breathes on larger screens\n**DO**: Use asymmetry and unexpected compositions; break the grid intentionally for emphasis\n**DON'T**: Wrap everything in cards—not everything needs a container\n**DON'T**: Nest cards inside cards—visual noise, flatten the hierarchy\n**DON'T**: Use identical card grids—same-sized cards with icon + heading + text, repeated endlessly\n**DON'T**: Use the hero metric layout template—big number, small label, supporting stats, gradient accent\n**DON'T**: Center everything—left-aligned text with asymmetric layouts feels more designed\n**DON'T**: Use the same spacing everywhere—without rhythm, layouts feel monotonous\n\n### Visual Details\n**DO**: Use intentional, purposeful decorative elements that reinforce brand\n**DON'T**: Use glassmorphism everywhere—blur effects, glass cards, glow borders used decoratively rather than purposefully\n**DON'T**: Use rounded elements with thick colored border on one side—a lazy accent that almost never looks intentional\n**DON'T**: Use sparklines as decoration—tiny charts that look sophisticated but convey nothing meaningful\n**DON'T**: Use rounded rectangles with generic drop shadows—safe, forgettable, could be any AI output\n**DON'T**: Use modals unless there's truly no better alternative—modals are lazy\n\n### Motion\n→ *Consult [motion reference](reference/motion-design.md) for timing, easing, and reduced motion.*\n\nFocus on high-impact moments: one well-orchestrated page load with staggered reveals creates more delight than scattered micro-interactions.\n\n**DO**: Use motion to convey state changes—entrances, exits, feedback\n**DO**: Use exponential easing (ease-out-quart/quint/expo) for natural deceleration\n**DO**: For height animations, use grid-template-rows transitions instead of animating height directly\n**DON'T**: Animate layout properties (width, height, padding, margin)—use transform and opacity only\n**DON'T**: Use bounce or elastic easing—they feel dated and tacky; real objects decelerate smoothly\n\n### Interaction\n→ *Consult [interaction reference](reference/interaction-design.md) for forms, focus, and loading patterns.*\n\nMake interactions feel fast. Use optimistic UI—update immediately, sync later.\n\n**DO**: Use progressive disclosure—start simple, reveal sophistication through interaction (basic options first, advanced behind expandable sections; hover states that reveal secondary actions)\n**DO**: Design empty states that teach the interface, not just say \"nothing here\"\n**DO**: Make every interactive surface feel intentional and responsive\n**DON'T**: Repeat the same information—redundant headers, intros that restate the heading\n**DON'T**: Make every button primary—use ghost buttons, text links, secondary styles; hierarchy matters\n\n### Responsive\n→ *Consult [responsive reference](reference/responsive-design.md) for mobile-first, fluid design, and container queries.*\n\n**DO**: Use container queries (@container) for component-level responsiveness\n**DO**: Adapt the interface for different contexts—don't just shrink it\n**DON'T**: Hide critical functionality on mobile—adapt the interface, don't amputate it\n\n### UX Writing\n→ *Consult [ux-writing reference](reference/ux-writing.md) for labels, errors, and empty states.*\n\n**DO**: Make every word earn its place\n**DON'T**: Repeat information users can already see\n\n---\n\n## The AI Slop Test\n\n**Critical quality check**: If you showed this interface to someone and said \"AI made this,\" would they believe you immediately? If yes, that's the problem.\n\nA distinctive interface should make someone ask \"how was this made?\" not \"which AI made this?\"\n\nReview the DON'T guidelines above—they are the fingerprints of AI-generated work from 2024-2025.\n\n---\n\n## Implementation Principles\n\nMatch implementation complexity to the aesthetic vision. Maximalist designs need elaborate code with extensive animations and effects. Minimalist or refined designs need restraint, precision, and careful attention to spacing, typography, and subtle details.\n\nInterpret creatively and make unexpected choices that feel genuinely designed for the context. No design should be the same. Vary between light and dark themes, different fonts, different aesthetics. NEVER converge on common choices across generations.\n\nRemember: the model is capable of extraordinary creative work. Don't hold back—show what can truly be created when thinking outside the box and committing fully to a distinctive vision."}],"isError":false,"timestamp":1775940988160}}
{"type":"turn_end","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":2584,"output":33,"cacheRead":2560,"cacheWrite":0,"totalTokens":5177,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775940985960,"responseId":"FNBTthE06M2/fLDOM/K894FBfAdQAVFhtf2TPOs9HNd4rR3159o2tLEuwuTiDRMfDsZ7w4YKU9RkkcwH8vgq1urXJaYIg3dir0SURvSrtn9Taoto7iVWTscFZNMW/qPP4QBIAmOpKIijbUNag6irlWV/xf2guGJ4ccOLbK/qJuI2MSszHoi0SeXVf7hJNooRuJps8kHtCuqRngxMtYGtXMwnjL5JkV18E+W71TwabHWtAl7+sNOJKNHZnLpfmgvIbKKqrEz30jI1inaBnGTAUInI1h3dor2nTll2IIeWItR9eJ+Za+F/4WeBo1qBCoEJggBqzzYxhjkdHrHkqbyULn8BtE/AA8cMCQTjQoOOPBLWz2ysz5YMMrToU8e7TC/VeVRlNTWE2BWGRMiXdM2rhRP8FMFBTQnd6A=="},"toolResults":[{"role":"toolResult","toolCallId":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","toolName":"read","content":[{"type":"text","text":"---\nname: frontend-design\ndescription: Create distinctive, production-grade frontend interfaces with high design quality. Generates creative, polished code that avoids generic AI aesthetics. Use when the user asks to build web components, pages, artifacts, posters, or applications, or when any design skill requires project context.\nlicense: Apache 2.0. Based on Anthropic's frontend-design skill. See NOTICE.md for attribution.\n---\n\nThis skill guides creation of distinctive, production-grade frontend interfaces that avoid generic \"AI slop\" aesthetics. Implement real working code with exceptional attention to aesthetic details and creative choices.\n\n## Context Gathering Protocol\n\nDesign skills produce generic output without project context. You MUST have confirmed design context before doing any design work.\n\n**Required context** — every design skill needs at minimum:\n- **Target audience**: Who uses this product and in what context?\n- **Use cases**: What jobs are they trying to get done?\n- **Brand personality/tone**: How should the interface feel?\n\nIndividual skills may require additional context — check the skill's preparation section for specifics.\n\n**CRITICAL**: You cannot infer this context by reading the codebase. Code tells you what was built, not who it's for or what it should feel like. Only the creator can provide this context.\n\n**Gathering order:**\n1. **Check current instructions (instant)**: If your loaded instructions already contain a **Design Context** section, proceed immediately.\n2. **Check .impeccable.md (fast)**: If not in instructions, read `.impeccable.md` from the project root. If it exists and contains the required context, proceed.\n3. **Run teach-impeccable (REQUIRED)**: If neither source has context, you MUST run /teach-impeccable NOW before doing anything else. Do NOT skip this step. Do NOT attempt to infer context from the codebase instead.\n\n---\n\n## Design Direction\n\nCommit to a BOLD aesthetic direction:\n- **Purpose**: What problem does this interface solve? Who uses it?\n- **Tone**: Pick an extreme: brutally minimal, maximalist chaos, retro-futuristic, organic/natural, luxury/refined, playful/toy-like, editorial/magazine, brutalist/raw, art deco/geometric, soft/pastel, industrial/utilitarian, etc. There are so many flavors to choose from. Use these for inspiration but design one that is true to the aesthetic direction.\n- **Constraints**: Technical requirements (framework, performance, accessibility).\n- **Differentiation**: What makes this UNFORGETTABLE? What's the one thing someone will remember?\n\n**CRITICAL**: Choose a clear conceptual direction and execute it with precision. Bold maximalism and refined minimalism both work—the key is intentionality, not intensity.\n\nThen implement working code that is:\n- Production-grade and functional\n- Visually striking and memorable\n- Cohesive with a clear aesthetic point-of-view\n- Meticulously refined in every detail\n\n## Frontend Aesthetics Guidelines\n\n### Typography\n→ *Consult [typography reference](reference/typography.md) for scales, pairing, and loading strategies.*\n\nChoose fonts that are beautiful, unique, and interesting. Pair a distinctive display font with a refined body font.\n\n**DO**: Use a modular type scale with fluid sizing (clamp)\n**DO**: Vary font weights and sizes to create clear visual hierarchy\n**DON'T**: Use overused fonts—Inter, Roboto, Arial, Open Sans, system defaults\n**DON'T**: Use monospace typography as lazy shorthand for \"technical/developer\" vibes\n**DON'T**: Put large icons with rounded corners above every heading—they rarely add value and make sites look templated\n\n### Color & Theme\n→ *Consult [color reference](reference/color-and-contrast.md) for OKLCH, palettes, and dark mode.*\n\nCommit to a cohesive palette. Dominant colors with sharp accents outperform timid, evenly-distributed palettes.\n\n**DO**: Use modern CSS color functions (oklch, color-mix, light-dark) for perceptually uniform, maintainable palettes\n**DO**: Tint your neutrals toward your brand hue—even a subtle hint creates subconscious cohesion\n**DON'T**: Use gray text on colored backgrounds—it looks washed out; use a shade of the background color instead\n**DON'T**: Use pure black (#000) or pure white (#fff)—always tint; pure black/white never appears in nature\n**DON'T**: Use the AI color palette: cyan-on-dark, purple-to-blue gradients, neon accents on dark backgrounds\n**DON'T**: Use gradient text for \"impact\"—especially on metrics or headings; it's decorative rather than meaningful\n**DON'T**: Default to dark mode with glowing accents—it looks \"cool\" without requiring actual design decisions\n\n### Layout & Space\n→ *Consult [spatial reference](reference/spatial-design.md) for grids, rhythm, and container queries.*\n\nCreate visual rhythm through varied spacing—not the same padding everywhere. Embrace asymmetry and unexpected compositions. Break the grid intentionally for emphasis.\n\n**DO**: Create visual rhythm through varied spacing—tight groupings, generous separations\n**DO**: Use fluid spacing with clamp() that breathes on larger screens\n**DO**: Use asymmetry and unexpected compositions; break the grid intentionally for emphasis\n**DON'T**: Wrap everything in cards—not everything needs a container\n**DON'T**: Nest cards inside cards—visual noise, flatten the hierarchy\n**DON'T**: Use identical card grids—same-sized cards with icon + heading + text, repeated endlessly\n**DON'T**: Use the hero metric layout template—big number, small label, supporting stats, gradient accent\n**DON'T**: Center everything—left-aligned text with asymmetric layouts feels more designed\n**DON'T**: Use the same spacing everywhere—without rhythm, layouts feel monotonous\n\n### Visual Details\n**DO**: Use intentional, purposeful decorative elements that reinforce brand\n**DON'T**: Use glassmorphism everywhere—blur effects, glass cards, glow borders used decoratively rather than purposefully\n**DON'T**: Use rounded elements with thick colored border on one side—a lazy accent that almost never looks intentional\n**DON'T**: Use sparklines as decoration—tiny charts that look sophisticated but convey nothing meaningful\n**DON'T**: Use rounded rectangles with generic drop shadows—safe, forgettable, could be any AI output\n**DON'T**: Use modals unless there's truly no better alternative—modals are lazy\n\n### Motion\n→ *Consult [motion reference](reference/motion-design.md) for timing, easing, and reduced motion.*\n\nFocus on high-impact moments: one well-orchestrated page load with staggered reveals creates more delight than scattered micro-interactions.\n\n**DO**: Use motion to convey state changes—entrances, exits, feedback\n**DO**: Use exponential easing (ease-out-quart/quint/expo) for natural deceleration\n**DO**: For height animations, use grid-template-rows transitions instead of animating height directly\n**DON'T**: Animate layout properties (width, height, padding, margin)—use transform and opacity only\n**DON'T**: Use bounce or elastic easing—they feel dated and tacky; real objects decelerate smoothly\n\n### Interaction\n→ *Consult [interaction reference](reference/interaction-design.md) for forms, focus, and loading patterns.*\n\nMake interactions feel fast. Use optimistic UI—update immediately, sync later.\n\n**DO**: Use progressive disclosure—start simple, reveal sophistication through interaction (basic options first, advanced behind expandable sections; hover states that reveal secondary actions)\n**DO**: Design empty states that teach the interface, not just say \"nothing here\"\n**DO**: Make every interactive surface feel intentional and responsive\n**DON'T**: Repeat the same information—redundant headers, intros that restate the heading\n**DON'T**: Make every button primary—use ghost buttons, text links, secondary styles; hierarchy matters\n\n### Responsive\n→ *Consult [responsive reference](reference/responsive-design.md) for mobile-first, fluid design, and container queries.*\n\n**DO**: Use container queries (@container) for component-level responsiveness\n**DO**: Adapt the interface for different contexts—don't just shrink it\n**DON'T**: Hide critical functionality on mobile—adapt the interface, don't amputate it\n\n### UX Writing\n→ *Consult [ux-writing reference](reference/ux-writing.md) for labels, errors, and empty states.*\n\n**DO**: Make every word earn its place\n**DON'T**: Repeat information users can already see\n\n---\n\n## The AI Slop Test\n\n**Critical quality check**: If you showed this interface to someone and said \"AI made this,\" would they believe you immediately? If yes, that's the problem.\n\nA distinctive interface should make someone ask \"how was this made?\" not \"which AI made this?\"\n\nReview the DON'T guidelines above—they are the fingerprints of AI-generated work from 2024-2025.\n\n---\n\n## Implementation Principles\n\nMatch implementation complexity to the aesthetic vision. Maximalist designs need elaborate code with extensive animations and effects. Minimalist or refined designs need restraint, precision, and careful attention to spacing, typography, and subtle details.\n\nInterpret creatively and make unexpected choices that feel genuinely designed for the context. No design should be the same. Vary between light and dark themes, different fonts, different aesthetics. NEVER converge on common choices across generations.\n\nRemember: the model is capable of extraordinary creative work. Don't hold back—show what can truly be created when thinking outside the box and committing fully to a distinctive vision."}],"isError":false,"timestamp":1775940988160}]}
{"type":"turn_start"}
{"type":"message_start","message":{"role":"assistant","content":[],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_start","contentIndex":0,"partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"{\"","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"path","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{},"partialJson":"{\"path\":"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{},"partialJson":"{\"path\":"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"\":","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{},"partialJson":"{\"path\":"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{},"partialJson":"{\"path\":"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"\"/","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home"},"partialJson":"{\"path\":\"/home"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home"},"partialJson":"{\"path\":\"/home"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"home","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home"},"partialJson":"{\"path\":\"/home"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home"},"partialJson":"{\"path\":\"/home"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/al","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex"},"partialJson":"{\"path\":\"/home/alex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex"},"partialJson":"{\"path\":\"/home/alex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"ex","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex"},"partialJson":"{\"path\":\"/home/alex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex"},"partialJson":"{\"path\":\"/home/alex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/.","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/."},"partialJson":"{\"path\":\"/home/alex/."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/."},"partialJson":"{\"path\":\"/home/alex/."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"pi","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi"},"partialJson":"{\"path\":\"/home/alex/.pi"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi"},"partialJson":"{\"path\":\"/home/alex/.pi"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/"},"partialJson":"{\"path\":\"/home/alex/.pi/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/"},"partialJson":"{\"path\":\"/home/alex/.pi/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"agent","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent"},"partialJson":"{\"path\":\"/home/alex/.pi/agent"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent"},"partialJson":"{\"path\":\"/home/alex/.pi/agent"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"skills","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/frontend","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"teach","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"-im","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-im"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-im"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-im"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-im"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"pe","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impe"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impe"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"cc","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impecc"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impecc"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impecc"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impecc"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"able","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/S","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable/S"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impeccable/S"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable/S"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impeccable/S"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"K","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SK"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SK"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SK"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SK"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"ILL","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":".md","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"\"}","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_end","contentIndex":0,"toolCall":{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|8+VRd1vutP8NRErfXTMZt1sx4EPWCi4+XuB0QNa/Gbnh2+nDY00o2h792A+Oo67hfXUznA/ljXMlTzBXycvf3gU8OVVg/JOq4xFxR+T3UHF/r0ZcxVba13FY96f0DyRB9ZEtu2WsWjTKv4WdNXC2UkboLIJQDz8M7+U6ezEZHXNHbiYbKu93pTecka2ucBwDKNPX6+5UenFgAcI3/wsiWGVugG4yIJdVnNvUzuabN1bVQD3XcFIxxd8tFsT/o95Ed28ueGYbmM2gXLN4MnHS9rSvOlTFxLAE64fqCWqoOW2+7mNSpmhxJw2+jj1hEldrkPnOo7i4QPS8Xx79PC30NKHuSooUhNSGvbQIaYbv3gmiaXapU2jZTm1zEjMrx6QfxXrpqnqYXdJCeHWXgIh3E7W2jYxDA7Y=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md"}},"partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940988169,"responseId":"7u4ZHju0w0AjbMIUvVIdunWjCXnKqp/eZuhRaQ5voBZTzCGtwP47sUTmpOgwoF+oG5bIJSBWa8wPlLldu5LgC6sOVWgesUAjRhdcW2zrMVDU6k3r1EknuFMVUOP+iIv9hMZKmMoInloP3T27u9PRGOG93/f7k7ViteiAAae5cEQ+BIlMzUPqJBE1XoI3YbOC0U6rBRDBx/kVxKKIkGDF6dX/XpalBNEK1bEiNrTSdprdiGI4Bw3gWdp0ejCq5Ftb1/CHReLnX39/hUgHURHwceOKJj9UgTDzYdtv4Sr/5yhvBjhTqinnODk2JDBMdDzcPDVBYk+0gjfyc8CdA9b8K2/3UrP9xsIStvxFnMIQzbBCllZZMd4+L97BGuEouuR1iok4ZOAK6EykNdQcpLTV9sqOPyCq/nx2HQ=="}}
{"type":"message_end","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":5227,"output":37,"cacheRead":2560,"cacheWrite":0,"totalTokens":7824,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775940988169,"responseId":"9F+g4Bl2XF0K8kMJuNdoncqiNJW0xBtgYGc87YSxOXGi0T3cRByFjO8vssVErIO1DgAkI9NSrBxI/Mu73C3Q7/cZA3lXWhdRzsiZrBkvbqTcrTF6ovLI9ahGDkKVhOBEhn1je5PU7/mXM4FjvLTUJh7eHzdfDCxtm1gD2+g9tLio74WeZl7EMWseM+9tm7rD2HtGBb5SiURT+HfC2oXDufWEaLGTuAQLwSSzyyJ3S8sYYtSgAhDXp7SP+intO/moly+BG3yu8v9d0sw84tLzJtSRjR8tQbvfr5sUaOMzYzYw35RA1X/m58Hj8TVVySpMjv9ZbnRylc0gHlmfemocPU4czBvspV11s+Pt3JL43GegwvUldBNLpDpMzepY3gg0Ppcm04Yz6INvKUXVGQg5do4wwjGA/9qzWw=="}}
{"type":"tool_execution_start","toolCallId":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","toolName":"read","args":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md"}}
{"type":"tool_execution_end","toolCallId":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","toolName":"read","result":{"content":[{"type":"text","text":"---\nname: teach-impeccable\ndescription: One-time setup that gathers design context for your project and saves it to your AI config file. Run once to establish persistent design guidelines.\n---\n\nGather design context for this project, then persist it for all future sessions.\n\n## Step 1: Explore the Codebase\n\nBefore asking questions, thoroughly scan the project to discover what you can:\n\n- **README and docs**: Project purpose, target audience, any stated goals\n- **Package.json / config files**: Tech stack, dependencies, existing design libraries\n- **Existing components**: Current design patterns, spacing, typography in use\n- **Brand assets**: Logos, favicons, color values already defined\n- **Design tokens / CSS variables**: Existing color palettes, font stacks, spacing scales\n- **Any style guides or brand documentation**\n\nNote what you've learned and what remains unclear.\n\n## Step 2: Ask UX-Focused Questions\n\nask the user directly to clarify what you cannot infer. Focus only on what you couldn't infer from the codebase:\n\n### Users & Purpose\n- Who uses this? What's their context when using it?\n- What job are they trying to get done?\n- What emotions should the interface evoke? (confidence, delight, calm, urgency, etc.)\n\n### Brand & Personality\n- How would you describe the brand personality in 3 words?\n- Any reference sites or apps that capture the right feel? What specifically about them?\n- What should this explicitly NOT look like? Any anti-references?\n\n### Aesthetic Preferences\n- Any strong preferences for visual direction? (minimal, bold, elegant, playful, technical, organic, etc.)\n- Light mode, dark mode, or both?\n- Any colors that must be used or avoided?\n\n### Accessibility & Inclusion\n- Specific accessibility requirements? (WCAG level, known user needs)\n- Considerations for reduced motion, color blindness, or other accommodations?\n\nSkip questions where the answer is already clear from the codebase exploration.\n\n## Step 3: Write Design Context\n\nSynthesize your findings and the user's answers into a `## Design Context` section:\n\n```markdown\n## Design Context\n\n### Users\n[Who they are, their context, the job to be done]\n\n### Brand Personality\n[Voice, tone, 3-word personality, emotional goals]\n\n### Aesthetic Direction\n[Visual tone, references, anti-references, theme]\n\n### Design Principles\n[3-5 principles derived from the conversation that should guide all design decisions]\n```\n\nWrite this section to `.impeccable.md` in the project root. If the file already exists, update the Design Context section in place.\n\nThen ask the user directly to clarify what you cannot infer. whether they'd also like the Design Context appended to AGENTS.md. If yes, append or update the section there as well.\n\nConfirm completion and summarize the key design principles that will now guide all future work."}]},"isError":false}
{"type":"message_start","message":{"role":"toolResult","toolCallId":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","toolName":"read","content":[{"type":"text","text":"---\nname: teach-impeccable\ndescription: One-time setup that gathers design context for your project and saves it to your AI config file. Run once to establish persistent design guidelines.\n---\n\nGather design context for this project, then persist it for all future sessions.\n\n## Step 1: Explore the Codebase\n\nBefore asking questions, thoroughly scan the project to discover what you can:\n\n- **README and docs**: Project purpose, target audience, any stated goals\n- **Package.json / config files**: Tech stack, dependencies, existing design libraries\n- **Existing components**: Current design patterns, spacing, typography in use\n- **Brand assets**: Logos, favicons, color values already defined\n- **Design tokens / CSS variables**: Existing color palettes, font stacks, spacing scales\n- **Any style guides or brand documentation**\n\nNote what you've learned and what remains unclear.\n\n## Step 2: Ask UX-Focused Questions\n\nask the user directly to clarify what you cannot infer. Focus only on what you couldn't infer from the codebase:\n\n### Users & Purpose\n- Who uses this? What's their context when using it?\n- What job are they trying to get done?\n- What emotions should the interface evoke? (confidence, delight, calm, urgency, etc.)\n\n### Brand & Personality\n- How would you describe the brand personality in 3 words?\n- Any reference sites or apps that capture the right feel? What specifically about them?\n- What should this explicitly NOT look like? Any anti-references?\n\n### Aesthetic Preferences\n- Any strong preferences for visual direction? (minimal, bold, elegant, playful, technical, organic, etc.)\n- Light mode, dark mode, or both?\n- Any colors that must be used or avoided?\n\n### Accessibility & Inclusion\n- Specific accessibility requirements? (WCAG level, known user needs)\n- Considerations for reduced motion, color blindness, or other accommodations?\n\nSkip questions where the answer is already clear from the codebase exploration.\n\n## Step 3: Write Design Context\n\nSynthesize your findings and the user's answers into a `## Design Context` section:\n\n```markdown\n## Design Context\n\n### Users\n[Who they are, their context, the job to be done]\n\n### Brand Personality\n[Voice, tone, 3-word personality, emotional goals]\n\n### Aesthetic Direction\n[Visual tone, references, anti-references, theme]\n\n### Design Principles\n[3-5 principles derived from the conversation that should guide all design decisions]\n```\n\nWrite this section to `.impeccable.md` in the project root. If the file already exists, update the Design Context section in place.\n\nThen ask the user directly to clarify what you cannot infer. whether they'd also like the Design Context appended to AGENTS.md. If yes, append or update the section there as well.\n\nConfirm completion and summarize the key design principles that will now guide all future work."}],"isError":false,"timestamp":1775940989973}}
{"type":"message_end","message":{"role":"toolResult","toolCallId":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","toolName":"read","content":[{"type":"text","text":"---\nname: teach-impeccable\ndescription: One-time setup that gathers design context for your project and saves it to your AI config file. Run once to establish persistent design guidelines.\n---\n\nGather design context for this project, then persist it for all future sessions.\n\n## Step 1: Explore the Codebase\n\nBefore asking questions, thoroughly scan the project to discover what you can:\n\n- **README and docs**: Project purpose, target audience, any stated goals\n- **Package.json / config files**: Tech stack, dependencies, existing design libraries\n- **Existing components**: Current design patterns, spacing, typography in use\n- **Brand assets**: Logos, favicons, color values already defined\n- **Design tokens / CSS variables**: Existing color palettes, font stacks, spacing scales\n- **Any style guides or brand documentation**\n\nNote what you've learned and what remains unclear.\n\n## Step 2: Ask UX-Focused Questions\n\nask the user directly to clarify what you cannot infer. Focus only on what you couldn't infer from the codebase:\n\n### Users & Purpose\n- Who uses this? What's their context when using it?\n- What job are they trying to get done?\n- What emotions should the interface evoke? (confidence, delight, calm, urgency, etc.)\n\n### Brand & Personality\n- How would you describe the brand personality in 3 words?\n- Any reference sites or apps that capture the right feel? What specifically about them?\n- What should this explicitly NOT look like? Any anti-references?\n\n### Aesthetic Preferences\n- Any strong preferences for visual direction? (minimal, bold, elegant, playful, technical, organic, etc.)\n- Light mode, dark mode, or both?\n- Any colors that must be used or avoided?\n\n### Accessibility & Inclusion\n- Specific accessibility requirements? (WCAG level, known user needs)\n- Considerations for reduced motion, color blindness, or other accommodations?\n\nSkip questions where the answer is already clear from the codebase exploration.\n\n## Step 3: Write Design Context\n\nSynthesize your findings and the user's answers into a `## Design Context` section:\n\n```markdown\n## Design Context\n\n### Users\n[Who they are, their context, the job to be done]\n\n### Brand Personality\n[Voice, tone, 3-word personality, emotional goals]\n\n### Aesthetic Direction\n[Visual tone, references, anti-references, theme]\n\n### Design Principles\n[3-5 principles derived from the conversation that should guide all design decisions]\n```\n\nWrite this section to `.impeccable.md` in the project root. If the file already exists, update the Design Context section in place.\n\nThen ask the user directly to clarify what you cannot infer. whether they'd also like the Design Context appended to AGENTS.md. If yes, append or update the section there as well.\n\nConfirm completion and summarize the key design principles that will now guide all future work."}],"isError":false,"timestamp":1775940989973}}
{"type":"turn_end","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":5227,"output":37,"cacheRead":2560,"cacheWrite":0,"totalTokens":7824,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775940988169,"responseId":"9F+g4Bl2XF0K8kMJuNdoncqiNJW0xBtgYGc87YSxOXGi0T3cRByFjO8vssVErIO1DgAkI9NSrBxI/Mu73C3Q7/cZA3lXWhdRzsiZrBkvbqTcrTF6ovLI9ahGDkKVhOBEhn1je5PU7/mXM4FjvLTUJh7eHzdfDCxtm1gD2+g9tLio74WeZl7EMWseM+9tm7rD2HtGBb5SiURT+HfC2oXDufWEaLGTuAQLwSSzyyJ3S8sYYtSgAhDXp7SP+intO/moly+BG3yu8v9d0sw84tLzJtSRjR8tQbvfr5sUaOMzYzYw35RA1X/m58Hj8TVVySpMjv9ZbnRylc0gHlmfemocPU4czBvspV11s+Pt3JL43GegwvUldBNLpDpMzepY3gg0Ppcm04Yz6INvKUXVGQg5do4wwjGA/9qzWw=="},"toolResults":[{"role":"toolResult","toolCallId":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","toolName":"read","content":[{"type":"text","text":"---\nname: teach-impeccable\ndescription: One-time setup that gathers design context for your project and saves it to your AI config file. Run once to establish persistent design guidelines.\n---\n\nGather design context for this project, then persist it for all future sessions.\n\n## Step 1: Explore the Codebase\n\nBefore asking questions, thoroughly scan the project to discover what you can:\n\n- **README and docs**: Project purpose, target audience, any stated goals\n- **Package.json / config files**: Tech stack, dependencies, existing design libraries\n- **Existing components**: Current design patterns, spacing, typography in use\n- **Brand assets**: Logos, favicons, color values already defined\n- **Design tokens / CSS variables**: Existing color palettes, font stacks, spacing scales\n- **Any style guides or brand documentation**\n\nNote what you've learned and what remains unclear.\n\n## Step 2: Ask UX-Focused Questions\n\nask the user directly to clarify what you cannot infer. Focus only on what you couldn't infer from the codebase:\n\n### Users & Purpose\n- Who uses this? What's their context when using it?\n- What job are they trying to get done?\n- What emotions should the interface evoke? (confidence, delight, calm, urgency, etc.)\n\n### Brand & Personality\n- How would you describe the brand personality in 3 words?\n- Any reference sites or apps that capture the right feel? What specifically about them?\n- What should this explicitly NOT look like? Any anti-references?\n\n### Aesthetic Preferences\n- Any strong preferences for visual direction? (minimal, bold, elegant, playful, technical, organic, etc.)\n- Light mode, dark mode, or both?\n- Any colors that must be used or avoided?\n\n### Accessibility & Inclusion\n- Specific accessibility requirements? (WCAG level, known user needs)\n- Considerations for reduced motion, color blindness, or other accommodations?\n\nSkip questions where the answer is already clear from the codebase exploration.\n\n## Step 3: Write Design Context\n\nSynthesize your findings and the user's answers into a `## Design Context` section:\n\n```markdown\n## Design Context\n\n### Users\n[Who they are, their context, the job to be done]\n\n### Brand Personality\n[Voice, tone, 3-word personality, emotional goals]\n\n### Aesthetic Direction\n[Visual tone, references, anti-references, theme]\n\n### Design Principles\n[3-5 principles derived from the conversation that should guide all design decisions]\n```\n\nWrite this section to `.impeccable.md` in the project root. If the file already exists, update the Design Context section in place.\n\nThen ask the user directly to clarify what you cannot infer. whether they'd also like the Design Context appended to AGENTS.md. If yes, append or update the section there as well.\n\nConfirm completion and summarize the key design principles that will now guide all future work."}],"isError":false,"timestamp":1775940989973}]}
{"type":"turn_start"}
{"type":"message_start","message":{"role":"assistant","content":[],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_start","contentIndex":0,"partial":{"role":"assistant","content":[{"type":"thinking","thinking":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Choosing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**\n\nI","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" respond","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" user","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" about","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" which","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" load","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" from","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" directory","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" They","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" want","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" reusable","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" pieces","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" align","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" tokens","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" so","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I'll","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" look","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" at","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" inspected","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" files","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" earlier","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" which","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" included","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" audit","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" teach","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-im","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-im"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-im"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"pe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"cc","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impecc"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impecc"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"able","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Out","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" of","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" those","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’ll","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" consider","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" which","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" 2","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"3","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" are","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" most","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" relevant","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" help","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" code","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-level","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ref","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level ref"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level ref"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"actor","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" meet","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" user","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’s","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" needs","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Let","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’s","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" identify","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" best","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" fit","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"!","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Ident","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Ident"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Ident"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"ifying","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**\n\nThe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" user","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" has","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" asked","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" me","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" select","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" existing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" load","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" from","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" a","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" directory","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" keep","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" my","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" response","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" exactly","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" three","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" lines","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I'm","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" considering","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" options","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" like","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" color","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"ize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" or","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" perhaps","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" audit","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" However","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" remember","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" not","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" select","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" too","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" many","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" The","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Project","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" guidelines","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" say","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" always","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" include","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" a","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" specific","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" cav","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"eman","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skill","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" so","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’ll","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" figure","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" out","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" if","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" should","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" include","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" it","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" while","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" following","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" user","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’s","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" request","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Selecting","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**\n\nThe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" user","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" has","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" asked","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" which","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" load","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" from","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" directory","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" while","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" cav","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"eman","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skill","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" is","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" essential","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" include","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" it's","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" not","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" part","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" of","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" that","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" specific","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" directory","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" They","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’re","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" looking","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" for","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" a","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" selection","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" of","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" since","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" they","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" want","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" reusable","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" pieces","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" align","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" tokens","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" This","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" makes","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" my","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" task","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" a","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" bit","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" tricky","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" as","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" balance","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" required","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" cav","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"eman","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skill","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" follow","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" their","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" request","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" for","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" just","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Let's","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" figure","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" this","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" out","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"!","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Def","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Def"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Def"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"ining","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" necessary","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" tasks","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**\n\nI","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’m","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" identifying","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I'll","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" for","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" this","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" project","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" It","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" seems","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" like","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skill","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" is","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" essential","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" for","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" pulling","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" reusable","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" components","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" while","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" color","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"ize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" would","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" help","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" align","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" color","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" tokens","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Normal","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normal"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normal"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"izing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" tokens","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" also","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" seems","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" important","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" should","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" run","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" an","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" audit","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" locate","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" any","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" hard","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-coded","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" colors","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" while","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" is","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" necessary","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" for","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" context","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" even","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" if","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" my","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" main","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" focus","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" is","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" on","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" code","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-level","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level ref"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level ref"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ref","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level ref"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level ref"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"actoring","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Following","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" instructions","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" means","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" may","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" gather","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" context","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" teach","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-im","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-im"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-im"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"pe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"cc","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impecc"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impecc"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"able","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skill","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" as","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" part","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" of","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" preparation","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Let","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’s","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" make","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" sure","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" everything","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" is","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" lined","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" up","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"!","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Determ","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determ"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determ"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"ining","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**\n\nThe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" audit","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" process","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" requires","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" running","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" if","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" context","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" is","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" missing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" involve","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" teach","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-im","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-im"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-im"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"pe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"cc","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impecc"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impecc"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"able","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" However","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" we're","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" not","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" directly","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" asked","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" do","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" audit","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" The","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" user's","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" request","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" about","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" aligning","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" tokens","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extracting","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" reusable","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" pieces","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" points","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" like","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" maybe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" color","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"ize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Since","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" cav","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"eman","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skill","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" must","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" always","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" be","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" included","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ensure","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" list","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" it","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" on","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"Why","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" line","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" To","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" keep","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" things","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" succinct","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’ll","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" likely","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" go","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" color","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"ize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" for","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" load","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Let's","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" check","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skill","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" files","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" confirm","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" their","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" capabilities","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"!","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_end","contentIndex":0,"content":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{},"partialJson":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{},"partialJson":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_start","contentIndex":1,"partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"{\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"path","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{},"partialJson":"{\"path"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{},"partialJson":"{\"path"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"\":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{},"partialJson":"{\"path\":"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{},"partialJson":"{\"path\":"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"\"/","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/"},"partialJson":"{\"path\":\"/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/"},"partialJson":"{\"path\":\"/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"home","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home"},"partialJson":"{\"path\":\"/home"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home"},"partialJson":"{\"path\":\"/home"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/al","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/al"},"partialJson":"{\"path\":\"/home/al"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/al"},"partialJson":"{\"path\":\"/home/al"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"ex","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex"},"partialJson":"{\"path\":\"/home/alex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex"},"partialJson":"{\"path\":\"/home/alex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/.","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/."},"partialJson":"{\"path\":\"/home/alex/."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/."},"partialJson":"{\"path\":\"/home/alex/."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"pi","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi"},"partialJson":"{\"path\":\"/home/alex/.pi"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi"},"partialJson":"{\"path\":\"/home/alex/.pi"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/"},"partialJson":"{\"path\":\"/home/alex/.pi/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/"},"partialJson":"{\"path\":\"/home/alex/.pi/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"agent","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent"},"partialJson":"{\"path\":\"/home/alex/.pi/agent"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent"},"partialJson":"{\"path\":\"/home/alex/.pi/agent"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/ex","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/ex"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/ex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/ex"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/ex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"tract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"/S","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"vCVafXll6Nxi3cKpqUIwkt4HTC0lFqdCUCJ14WzMG5Rju6NPDG2qQ2aHC2ZzOjDgDQtbUOys7rniRJfJxwOuxXIKNlRcxCpUNXRVzCIoF7YfC+pYztEvvmHZRMCb/0sWHqYoZ7lRLQueoMDintaBcJjn+Wf99eX0fdTdL2iUzv+nPm3gC1ypMp1+hY8MJ4YgmDCK+ZSqrGmLIjqD9XYqwMsU5Fu1kHptsOY1btirIZSCkUeUIfjPrYUcKmE5JlLVlWem6UCvgTLsot+ml365EJPFMrkZt7RXL6QfJ4mm0NYzDbDZuRg0qneIeGwpA/huo6Tr+TE0yS+9bohZtWTUcmWsCeWhtmQaswyQs7fr0GIaUskOA4BLNNG2fwTbBiyjV+AhC15A8OeSDdLdMyH6EhqDYDmIIm/RmZuFunirTtsyOa3rjwcuSP/40h6OH4JvwloCZXtY8jZ0k2RdUAj0e4dS1h8z12i7VREiMvdPp6Ui2rBsgCsROMiywDn3uHib0ecCg25Rja3CYwIaDttd99TXKxw5rrr6mUCpFJKjZgvqrUXTC9Y2MEDzBZc7iW4XBjZdFsGs7fQh5ceyw8DzVU82Y465/0xmhedamTHzJLfTC3pJ8OsMV6tCdtFaRjJ74w9/WVI/UDz8IGg0/vewgQ92CaDGoqZVguQ+utu0iPVM1JsdwGPKL/jIAeGFNvu3Jq2dym2VNFHvBu1d5CjlrzLCB1U6hEkwZtYcu39Uwr5Zg701IrQqRpsXIdtH1CyUjDLQiWafhypHTlEl8RTz5RgS9eBBO5RX/pQeqD1yUODPXEd3pFf+r8txrlgh3uBxTriWzdoFxiz+g/yS1Bryv1zdI+cdqBlP1zvcMb7k9f88bdXEH7hRk2LvWHqZffkHJ1iw0wxeaTBwzuaaJuu9lPhgt/Y0+TE0P+2VZuKgahyGJ/o/kYkTc+p4NeoxwOgPqQxVyY7FQENODwBGVSCgGW055x1XRpbkuBAKWB7dhRsYrEa3Ix5Dsm4Toepexx8hR/zm/bSYw1oYWGWIDFjdyUqo0D48C6NYPVd604P5wqOUXvi9KHTumFlRdehExDoMqMC/Ow4IfWTPA8ai3rH5GKEwIELcu/NUaHHOav2ENd5QOYCXbc5HReiWupIH651V73ax5A+MhDzTH+ztTCcwQb8qgn4a5/qquVVb5pbMxqNprAQYZdeJSiRqZNKHUYh8vd7/NknAwC1LrMxomFnarCC86qQs6yL4CQcM/fVxmCD7o7o8DIDDbTKsorNcqkgOuwVhA8IfAzmPCiGtuRj640UKh+nJnIWsKzI2JF1XZb0X9QMrR1Zi117y8C0cYJRtpUDzCKUJBHKLjKvsFpzSg/IH1OXZjA3E5YIs+qrKuwJZpWvA1CTKv0JkCh955yIIF/+lJWgQneK9aPG3R0pcJC1oFfbREjXCA20lAHSgkyxNcyVeGBfhJvl6NKA0PLCXKRCgGEzRmTxZ0WxKbkZ8RZhaiz9al//wFjQ10CI5/IPgAlvlAn0uexPV7nxPnu6Qvr4/CG8vPao5BL7BRLGsBXXOxq1q/DLFz7MmtLLQxFg1KXETaGVTcpnWBMrbIlvWUq4BZa/JDbiQtRnqrTAKlQdMaHXtA4Lz29oJozrXONwJbhB6CibrlxbtULtLB2xLeNpCq1+Wo+H8sO7iYkNQ2npecj2ekTddxDuPPpxbdmGNkXscl5igytftBb//P3EbGXuIh0QfqLvuwwZBkr5SoIRCJxbJ4KjhJzHLKtkSlE9mxR+oyThNftafbF2qH1DwcqSpvd9vXDBW93iYC8SxsMc8jpJOZWPiid+bCo/FSVi7I17IB2F5AOGv35TvZL/ZSvz306PP5sc11J3mWd+AdSrtbKEoiInoF6+lXJ+jdgQSVwUEqQoI3IrvDzy1WoGmVeiZKPGSQvoT9zgKriBefFSyd64Qm021Dr28Oxvbn7HC0CEBsstUW6tLKD8hmp0V8wvi/qh+/ygUQZ3vR7WetyKBCxsxt9TUaTlhN33K2fermJZw4HYoMcXEEMogeEIaWcRZnSnXzsqC0pipchPIRsG7ZCuAXfpj2gFOaxo5FFTmxwEl0F83Mie8Dfcwnw9ks6lLxXMAzyiRXHKNqSurNRLGYNdjUYiqWtLV2sboSUhKhkgm+6ul/ie0nnw6Vvzg2zKhODR36P4be3q2DFrq1TOpCINzZ3AM12/nVnzjp8cOYIJ/8eR8/kUk3554/fkrLmoaCs/3yZz95BGOkDt77YOBCq2b9GQublfKDKW5YiAxPGeiDIluSPhLch48bs69sPXyKLCvQNydTfgTWS4vcNHG5vGtdsxka8PxrMfFVRwjy7m/2Zqa+SdTW7OKvulXsnfecrfu9khCozFoy+WmsdQNkd55GQjSIvX+U7NlfHqXIIhKI7NZ6O2T8TKd9ON6dcY+9ndYc8YEqAiMy3aU5/bFn6AD//jDo+0sW0imv0UH0dRLMnvekVzVq3vQ4p0qCBlqX8yO3f0a44z8Ue6/yHPcDPDiZiHe7y+JpWmCyE7ij9InvVBg8MMkSHgif7hmN8AKAMNmz/0obnN3B87zQCnPLKvgf2Rk+S70UELXIHPPilO2jHtiWREDHzUnYM8EKKlAupHckK/UTpDyU+bkTTT1rum/x6+7rFu9Yb8Ma0V4qFZUqAYxn/+Tcdg7Nb9ObjJKaJ90b0tfe1oTY9Hnc1K5P3+wjSWvUOp7ZQMnfEL03I9sSB1cvdSavJ8t60loWAsLY3QefQ/E0kWMnZDvWlkhCI7NnyaS8mTaSD6GV5bH7EXp+uD6/+jYt7KC625GkN6ButYXiVklSMlAw1XtG5gmvQEcmW6WvC12wgT0inQI+F6v889e69pbXzBTyjiMU/Bt2oFJgDzzkqMLAjNCTFVoU6ER6Bmo4L3PJ27lK9LvlRGQ/OYRTuzFiOPug8Pk2klnl3+gVEVEfhVC4GJv/OeSj545fMTWEmjTl0OPzqe/QjJzEBcaFkxRPGmR4PGYh7VLWeOmAAPfwZUOct67nICW/BGjCITKhgMGq6NxjUWb+dUsJ1LbtuSNFAMkXQ1/mrAqmguUxk+Mkz01BQYGw9wHsqn9A5ymZmPY7iiYgv8R8BsT7DZyxaWWVWTS+5/TIUc0psQ8ZKDDTFH9LgAZrpsDFHqxWFO5ojB5javwWTE894djmAQ23lm2awAW6DENvdCdNDDpC5zppwWA1YWHkP4bU4ztI+J8QSWMNnSSbyp9dSAA2iyQk+7YfnO4vAxMB3lI5D4fPX8VlfNgDjvD51kyM0XQEXFxVCi5KkgMe/a3Emtq57YMKIwk+ZlLBLtZhSr6S5Ys+vXPzJIv6abG/nsCnGJLwaDGEHoN+SgUqPlHc0ubj7E37nHzABmDSZPuhi6swYU3ZpNm7iCmEiUg8q5gixp2fdrPQd+xM2abMm6ozot1N6mqIw2kvNSswBPGPMpLtUkrAs2T2nHi25o37pREwsttbThEgn9X0eBfP7P8GKmNFo1akF0RKxpD1QnLCwDCBdWowccyJJUogus8wjBDGzyLp2kc92fPcNQ0BRbOWQVsFEc39dPgL57EzmE5IUb15T2CuOsruKWlZEWSHXCU9KRalvkKHQK2A3Ow2RBTKu1SyQyJv3O3poU5DC5aiMTM829811GrgtYaOqDYXx/h9lDzXkco5uI8fBIygOCKilySoRNkTI7FWRUv93CTjU0fRH+QbQA0bZoRDjsskateU6IbOplLV0mHVMO5/oSbaMy8RaWq+rSO25pFRbE6zZ3NUqrpHwC2II9KmQQMOPUKzey299N+kiwEz0hRsnXFXKC/Zw9CS8nvf78AeZEL2++WV5yMBRGchoBHLoiq4YJOIpHD6JuiJPaOdlIlKv5UI2bDqVllqe1SIwf3twmmzXXHo+LWPkiTz/xunZWItg9qbmEcNEKa7l7MOKc4UGRWULM7vdp4IWpCdGDAPAQq93q2PA6KDBnncVrNfXWQoOWGmgFm/SlsS0JkvsX55r0FmN4otlR32yyiUv+pRtNo8rHYECDXp01hdqM7jM6gJ/g5Uzx/YpB+eWLVtF2nROv57F09xT2PJDv8hiLyHnLQw5sC0/s+zJsOVSjUSIXF0YuTtlwKN6necUkRSZ2KLqduxpe8o2rzFAF2516heLVCSrDF4+BrXTlVCMFX180WsN+djeMItUXMIN2aHbGVDIeYLcOY3ikFrZKkQioQF0zILVfsV3Kvqtu0APhaF9/hwwmRqNkTh7w3sg4h7GOFCK2aaI/pVugpDNgqDB6MfyE9sonJ7nwYuD3Lj4+QA3UuBX5zV+aud8lBewfxbI37G6Qm7zmGNv/MVH/O8uDZs+BA7VtxnG83AvL+toyHCkYntWL0PblQi/QLk0S/My2A51q1BUYYBJYxwkRUJw3RYTrMwg250orWKn08OQqtnZRit5Q+clb6TZl+D+38u0gychMt+G+DMIS20LgWt/rHUm2UtzHU8ldo46xCeDem8fBlH41fk3YdQpLjXLMapJO+g3Mkc1MRmuBg0s82oVVvejNsWN+4AtnYUeUdBhZa8D8YAQuLcPeeQnlE6hlv4pzzEp7jeUrpWUILp/D8Ub2O2MBbRpA2OdXrgUwSScrJCS1B0gyboxiEut0p+LzfXmTBdTB/l+J1FvFQT3AU9Ry+cK+JLeU1/rsg9cKfI2wVaTyX2R1rgDM3fEquiDmhxs3IZHBzJAdadkscXxQd5wq4ukrkbiZCbwx5KN955YziBgUIym6PKorr3S9GHpMvpnNHREUS02OruurmI4opvDpKPOAmR3ZnGrhx3m1rQpT/lC7FJhOHEcvvx/paZFRd+pP70N0ZUr+MLk5lTwyHpiP6gT6Sq/kyyKHL0xkL8SPSg6/HWRwufvim81G0Kmq05Qp9pCVf43JeBt/Xl/bNt6d6PV/frrLmt+8p8L2jRFfkSAuJ3HV7NQo7x0v30xLmQQunRCqZorQkfr9iQQnyGqTyGSMevALbminFoiPY6H5cHE+v9hf6q+YYlQ/+x6FVIEEzkSns+zH+Y/Nz3D6mAIsA+QEcY8+iPajm1qo3JORCsXMpQLHlBgliWUAU8w5rDo9jXuVoJnLYDDGcwSFqGHuObk2mlm94YLkfT0hw289OIUgfJLYN80ChV/G0lQpgScE63A/xXfK1y1nxWW5ZbZmw6zZ2CXWzMlj3TNtPVKSJwIqrBzPPeaWcmIRbyXfA3gKs9ZYcikAgm+jnt8JM12Z2ZemgUqM6Fof4/w169q0mkEn8bzbK1kJhGTH2BaKS3P5pacKF7DNXTfxiWfRLwYEtC6JAlmjNHncuV/68BF+/CAbslC9ADca+H1jidbATPI0MmXbFfatVpBPi9ZwlxCV9w9AFzoM3lqKdVkriz/yl9necLHkLeHWtEKH4YjunX5gWLsxdZvGU/LnzdYy9gUHGMNgjDm12xHn6Qz/r5im9CdNr2DfLnv6Bya7wfo9fm6LcJUVKeTVBNr1v/6+coCdFlP19yGVEM1Si0XfgDQPf7ph15ELzvZxSS0QLvnq9l/vKbaXF3frzteuElhPEcAPNDKg/61dWY8JQyusQhQF7I5N9gx6lBN/PPumfMxugglIsEUll14rwzI4WBsCIyVfVUssROE1GouGsIvQ+4w+VRXHv1EvmLaom6ouwCEoTxUxwSbaDayimtnlU8gyc/dEcjAbJAuK2NoJxXPV0XOMZMH/xYFxJev8MxpI4tqYV3Cj6yFaQVyxfFgD5gfIgQKWh39T6OU8IihBuwLChIeG6asG6aqKdHNOvNNVOrXWczi7kWvNAmd4n0yV9msX16fvW+9TokhOggEFU3ZbX4mMPIQ+vKAJvuKUwGednE2hv4LEiUsEU9QqyIOhxVSUCEyJL55kQjRCg4ZsR+KI2J4w/ykJBI9s2Kz3PD+05hMs4C4jdGwxH517xzpckycKFGpeWwHNYWj8oqlyU4fQWKiOyoW7xfyMyCa5bKIyvKuWQjg1ucXkrO9I0ibKW8GuoAVLI03pRs/W0hO1sT1n0Bp3D791IoyVpuixZGRhRFwZrxs47llwGhq1RmZkvpw3ZWD+Mui7It0HWChCpgOrQQ/CM58JvRklMOFV0N8gNEeO08amtv3mPmFnVBinmCRkJt2UuxxZhWa7nisBe92rWM+UZ/aqgFQqdU88ocmvjJAurjlbi5t44d4vmc1RO8mNKCF/m8F0kmnEajje0s5+Ru7UILHxSEYPJqv2xefTntuLmKD9HOqcDnjzOPwjBx+yxALEOGATfl9N2MMYwtKIE7+4RTn9EP1UWgFULoBqFSdAAQRf5c/Y2UtEF6jvye6haYYddCIjdfuIuDwXkz9ebgezRqTGF2floJA8eUCpsPeAzj0VeeUwN+DeHWlWZzV911gkLLsaYZWeuEGEEKI3NmAF2CdjHcWhsrZjrgLNRjdd8eq7+COpv6F+cmsdSNpmN+tJiHPEwBK+/b9Et2pusPyaVBTjFvwFXcsZ9Vnaim4nDfsCHEUZSmmb13ItpZrZyuzxluA+XSFW09iAQ+t3p9DPEzJP4G0W1tJBK5c1079CtRN+g0gMJhfMRE+zjhTrtVNZTSMDtpWHdbmnM7vtbR+WwgSLh1J2dAYVkzwru0zRgOWIzI9SGQw2gt33n3Z1AWGUFi82p3+hHpWB9ob0S3bbd6h5ID3rNbxgjkfJxCBhSXJoxM4vykj9cQtgYPe0ObSq56phcErHFc/igXnex3Dgf2Ry92oNH0DSUIjYEhlgLYqCnjEW1DkRRyIUl9MIs0lk0PzKOpgDPPkcjcxtIFCLbNl1i185/W0qbztO7PZUojuH+QzdxkHNO3FA2InAS2UZ3gHhUiAyhis/wRc1iek49cSUGSycAUCzD9qzgLJPtv7MwiKOLZ/YWgJRy8ZSyxqWyNexCzHVJbWTY+AbJNX9vi1PcJQo1w0Jq1hVpgTgePPgGS07Ymi7tTXMTDzU3xlLMI1KbV0aDir5mx2ohVgSFW5Lu0u9Vna6ddaievNHml2+SlwscAizNm6N0vPkThM+DvSKhBLrHeLIlgg9++8Qj/Nd+5fLwnCu2ITMXU1WXiFo+74FzYlbcC06IvEmm/XCNFwImLx2cWIb29sY70ExN4EjHydAfvwKYcGy48Kz+jlMW0XxmUkijoyc/lP1tKDlDLgmpqT5XaFnCjdKdqOLggmfjj4OVLoA7yey3sD9rGo21G4M/M3/jbu7GXp5cpOfhYGLhJqqvGze9fwcmhxWlOmgN0UBTrBQs8oXkv2fiwxCViYmb746rgUZvWRLi0Ss4jQpYeiyISCl8nyPNAxluqWyfmDkvXiXtmyqqwnR8bqYAVK346m7ATt85YlBAB755Z7eOShxZXF3+roDAtnGspQ+ygCsAQwn4b00bfGrPot9T3pzAxlLeOcYg9uYNEIitp3xb7HXDuCYCDJQjEXUTY024ZpD0RKsByny2iqwWNzomT7z2zJ2/JJIshh+z94fwDqx2NIWR9IRMx1ZLsGqycczKl7qCOCTd8xMzrKIKW5j6ITweekuOWaJiT6kkwMaKI5SCuCYL3MKE+lOWqyUSs0mML2xZFxg0EsgWkXj8B6/6LONu1J8Gn0KjwnysntT16N44m8/cCG5T6BLzYtUvABP0ful1S3GeqQOsT8gbj5aJcAXGyHpCj5a3M5xHRcXYgpcdRXiKCX6gwCYwpBa/W3fMX0qraGom6f3YbHDIJLY73RAPjlV3kNpq7+ooPQNirjNgWmH81PAdxuxiwZEtUctC4nvKbWh+LbfU9CKbgAFCjygodMDavkUdrvVQBVBdJlUwEZ4CipKf53LpY3AUWVyeXQGMOwwUior1bGAtlOL6ipRE+RqoP9cOhzts6uq+c6ADqnAzWowXqnL7OeFEQg/SWP/1uxNTmh7W8vsv6UYe/AdT9tn5sxW3pThYBtWFibd1uH3HcdOt24+0E2S8zBAnSeHz2EeDavHrAE6aCRI+ZVBeBhbu/Bg7ui9dlEO0kIkC/1VcDkeUv6IkNORIBfu0iZIZYqkk8ZLIPiWZedb8eP2sFBy9V2RR+Auo58hMo+3f+gEoPZTtC+cSPqvXYS4SpZKSykjxCveeC6VEEfkcdzTFMcV4eKKlXWg90WbYWjgU3o/88s9vyNqqGfNqiImLzdhTdrbDewdQTCyq04rrXwQH5mLiD/Y3RFGl8dHLcyRLkFNiyP4DVJjVrT0Ek79EkWqoDbtdAAjjAgO+j5gzngn8mx4kd3DgecbKNr8/o97e5GizcIgIOMyPE3X4kl2r6DHOqnb6RfOvrkRSKmQCs9fRMhAAM7iXgIBjMFM/Tok4syDb/cdQG8WkzguKS++iwmB9IEEXWyMwvvndho3Q6GjJzUiF6/HvXMM0jGJHQdYPhjsptKsRZyLgjYjVjocqUGw0cN2ckGSrfT/PIX4OowoE10EPxHXummj7SH2U1p0rWXXctcNnDTCTMkG04L14Hs+Q6vKtfWrH+KHQrGX+1U6k4S6vW+kXvcwWQ2HHFiHvIvUyqfs2NJU9oR0bK8Hl0xZTvKW+DExGJDd3DKrcAFIDiJifejMJ0AgrQt6cX8qTP1xie9rhEVCtIDawQgoWVTxEoH+vl3JMbVcUpJp+ITqb2dLB+nfrmEFL1OGs2frTP5jyt3dlQMUo6CSRbhLxgUHXQisYJ9YncVU3/ZbX1XP4xPx6mIgWCxXZ2ZXTNXVRBT0wzzDSr9oipsXQUM+gxwpB11y1wSZeRMMDQsCtssW0Kc4T2yEKZrrTx6D5mkXPk5f+cG11x8xDxTo8aRMPentroUJfVCnLbGXfzb1NmOIiuxLev3+9q9hTaR0zfooQdmjQyD8YG8VuLPHUBO0C/sqMSlphoj7/3zJtT2H0tebreHzOMdYbCPZVv0763ZL59tLuqA6QlK6xdYqfiptlWcMoXgCnjmUTqOnKdEzrTQ2nXwbL061tu+k0oDgGfJFi0/GPa5w5lqeZUM0YYz0On/OcY8zjI/FqyR/9nwLB9QK74UIN3a693+n9hnfu/MsF4AwaNqS7cWu6piLLYj8bN12IcnV4tib0zNqDMo/ukjOd/iUKYF4KnSnKO30O07KkCPEZWLWS8dyG91SPJ0je4jqLzphJ6Qd0/8MNxHehWlmYjULTIM5+JQWCHk6Tm18xz7yhmlxFkR3xQmSttKvAENk4OpA5SRb90948mKn2KZ4v9Pj7RQbXtgV2Q9HfO7Zleyq1Z2XyrWOEipn52M2xzsqAEkCxM7C6ln2y0d1mRMJz5pf1LEF5RrZKp76xDXq1e7/Z7W947jahvamfF2DEMpiDHAbf5ehkbjR9DOKQggGL0hbcOsePGDJ4cijhMSc+iHin5FjfU9Ta0BssuVXSh5o8FuWsmZLNJ0t4kcvwBfWKV81i86Ysx8A+iBmIswOOlF413p0w7/a96qs59BYzkEu7B1r/wbpO7AIEBzVhKkr9IEEYhGYavUaA0MqtM+XlfisxsHr6hDbEiYWltO87EcXKvc32UT5L24ynGPxL9SlW3tLP8HnIfnU41pzBs1dloYQZAVQ1Z8l9cFP47xfjbuO6PM8rmSPWIq9N+kohpXW0H67mCcp8i8zG32hisXZJHmWxsSK/D3dQNpmCMzJJOdAUqN7Axw/U2ehgxs+c0pLwDXDxmztIz2bSJ2XAoo9kUR0Tuv8e7UlO/DAJVlw3HCYJNORVzQMx4LC5LP6+oJOdbc4ZsvC5+muMzsXthTodaWIiNvLFkWQ3VPlj1SZQtMVQ1ZKLsBpiIYMxBkqM6NIOWgnplNAeyqmwAaEPzrnjnlvNaqOXL2PhcFi5BBjHNh7kimacUecUr6lVlhefM6ouqJ/6zh+mHcz5nsWM3Wdt2aQAYXYYK8/LiHPT84PhlX/wu5C2hxa8zV9Rkm/gguRCJu6jxv/OYvyI7SDvZARp2RRhbtytALE0XEDaO07jaG5e7JqU+v9cWcMJeZzCyJYtrr/KugJcPB8xxu398puUBD+vNAGr3gCfTaFNOOkRpuo2NkPk6XlKGMLkXo31huAbyYMWJL5G4Lf3QWz0mNJ3j3f0YkmR+5mm7Qw3KESWRhLVIG5dcZKI2GqxwkvZUJdXd3N9GLabm7+tAbRt+x/iMkmxQYdoYIuwTHDNdw4KppupPOW66tVWH2hmnaRbJYV8aORaxj+Q9dt+5+SaagDWL3bKOk+zbrrRAGx8oNyPEopSLKl9SABwkTNHbKJCQsqQZUL/qyY4RPacXnZ+6znMBVlh7ymaHUi3RRTAI3jHw5Vt0MXZtuFu0NKU6bRsAa6wl30R/QvgtKQMxeh34QMBHOT4ZJ+k2bCvLkWLx1s6EMIs3uyBq3padXMBM1bhUZuIC4KDh+G2Oct+59ZybqO45mzL3HgVAEuFvFDcHxu6GJE9kW5tpQdHHNzRwBuvH4DbyzgMH0xsOwKfRzhDTFEIFn11CPMY9oALvXMHv/BYGjSCJ+XE2acVla1APdSezGugeEXPZP/r6NcHCaW5hTvYF5KU5AhfICNy17kS83MIKgrdi/L83YfPrDvxh/ig0i3BBYex1i59zyedcNCDylWxelsBpxcCQNG8TXNGdLOVYE65Psi8l/7LGxbyGjMtjPSIRyN10i74XLhIt0MAYNUg8m3cWSN2QO1DcLu4Apzykz1vhtpfEuqNgV37PFr7bOpumi0FJIk13SefUj0rcmIXdvRHO+pg+DzyEBQ2a34IHT/nnK5qALvua7k7P8F9NBDVRD5I2x+9ItMwVC5IxWkQiqPrxp4dkfxNc0JcFE/l4qU5LXH1a4F1oueTV6BGgt+zWolC+5jOQfGTj+EPyCXEv/nJZnTfGMvxpIfBvN8vKIF1MlYztBb7xyfLFf8wHHXio44lPURb73JjZQEe7eYiqImoIWV83smukbxAiuU3gw1+tv7v6WN++wnrO3ONbJ3gvAqxgmqRFisLzpOrf2EeiteaDydzhYply8c4k7e67VE1pem75auIQn48gI/UI1MvEhb0nLxf1BqB5PYAyEEJGqT+ZN1uRl0bK4gs6DP+cxOThDbihh5O9e5u170JrP2FttmNe3qg3jlN8fpxM2rRd3Xkh2aQAUh8B6Ia7Lt4Dc7Dl6hCb0BWoTQTWRwj8utTWYnRn3r/m2v/yCRysDLlsQnpMFFKz/4W1hElxm1IFXZGkeqyBU6dgJOYZPNCmWUjKXpHoLQGHJlTadbwh9FdWA+SBaGprpc/coztOTFG+AyZ64Yc0M7G7CZGi0KnjpiHU96PyeIkWVkBe2GtltPi9KS316HF2VgonT9Q9Yaa/cdNG9uzuDuY4KxYH2KNvWw+9Bfyc6OwFPaVFYoVR+R068qlWb6GNXLHAQMA1Suvy0hcjX6FdMGOMXZVEkiYbX/JickHuRh7DP4twUFONWVJ/dIvc0FDWs3BSt8hGMf1q60vC+K6JH3QCEYDvkjjbUU0WfrQDTmgC9YrfLujtHIFJjU1JTYB4iVLbOOE+7vVxUwgRng0gRFDmOCajb42HX91z7dYmeoxfV1GmglTXJFa7JnGMVX41t0af/XXmt26H2W/+acTtnZYlMM/mhW1F6Qbv5KwrRf8zalowxyeH4CgnKpk3XlMI14h5enfOHb07s1U3s68WNB2921/YKRN9Jd93Yqcir/McUhtnnnGaK4ISYNZqRvhqv+OxbJf1twH+mijKY7jXkmvOezHNQ1c5rgIJ6i5URMBNggaQN7iuPdzAYW4feEI2pvvpE1fO2TZxGEoH87m32ExYjhPKWQt9C6Y30kgoejf27x4COfOFaswYhHLo+ES5u9afrNfoDei4JxxZ/8l7lN0HB9j4TtZHSY0o8ogMWVQQItF7itnUKqgG8RFTiCcj4hkjS5KJbI4bra59R9t3fB0iUZI/vA05etDBdY92yovN3Q+ZmrnEYFL5bRN6j6guy2n0yK37ePqbIUkT1iA5+JxhiegsmzaxuLullaNjVPVti1i0f9LBpE0iuEFezCKZ4EX93UrTv485oFH98WCnNaPgisKpBcO4T13PHTQtQmb5oXjCrDipVCtHkRLU7z79SYn39Mb1J46bOuDE4beIW7Sn5uUPopq6jBu6eobstPzXnzQ5nVdqvQoN/kEwydxvs6yEJufXVtTNrlVAtLUc9QVEMHRBnaA8Vlr+ZIPA2qL/ekG07IH0eL1U1QKcU+iagt8Szt1IO1BApI4RoDlKfOHAn4rSlhPSZjJSBFIS+LHKYSDzSRWW1Ay8Lw/XZmgldkAffTLdRksTgrfomvtC0n+XJKx0PUyh8mcLO8JBXUfKYcrcP6ykATPw4AxPLBWwlLkaMweEoAZgeFhu7SWJYnj1S9f/KxIGivxgmwNWzyKd3BTcPW64k88foGNKAfv37FaXjs2PwL/wJGDzb1D33OgCN33+4EbUG87OscxyO1Jb7YrV9uWfuAbEm1Q4wEPirp1NK7BNnkSIEocXX/x4sgfhecCKZN7qd3n8FcdSffy4N8QEytFTdO8OK19LnBa7wFIsnQI5U9QMDPxwazg022vt+BPZcoLBz9VgK/WtGvcfeB+CdMpG3exrI0ItBQxzqY2DBt6UomQw7XMjWS+zKvPI5wanVBaz9Cd5b5Knv0zyBzxVV437um9kCQ/Zzl++sicoER/1L5UHYHjoKZoMt6rp00mBFpBpOW4c2I+E/g82OZV9q6xzN3VqXlOyoBELKoFEEj+i3E5Nna1V7ywCDtREbNLtKQ8IC5nXenbgKAxYZsZS7Oine4t+HIU5YQU9hgTKgEoTYEeqTv5JJBSc9arpEk2lQG15ALpBRwdnNXsE1xD8qQvtkp84l2XZl2ojFiduR04e1BIbe+D0Hvi2VGyaEpnCdo=\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract/S"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/extract/S"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract/S"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/extract/S"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"K","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract/SK"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/extract/SK"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract/SK"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/extract/SK"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"ILL","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract/SKILL"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/extract/SKILL"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract/SKILL"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/extract/SKILL"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":".md","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":1,"delta":"\"}","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"vCVafXll6Nxi3cKpqUIwkt4HTC0lFqdCUCJ14WzMG5Rju6NPDG2qQ2aHC2ZzOjDgDQtbUOys7rniRJfJxwOuxXIKNlRcxCpUNXRVzCIoF7YfC+pYztEvvmHZRMCb/0sWHqYoZ7lRLQueoMDintaBcJjn+Wf99eX0fdTdL2iUzv+nPm3gC1ypMp1+hY8MJ4YgmDCK+ZSqrGmLIjqD9XYqwMsU5Fu1kHptsOY1btirIZSCkUeUIfjPrYUcKmE5JlLVlWem6UCvgTLsot+ml365EJPFMrkZt7RXL6QfJ4mm0NYzDbDZuRg0qneIeGwpA/huo6Tr+TE0yS+9bohZtWTUcmWsCeWhtmQaswyQs7fr0GIaUskOA4BLNNG2fwTbBiyjV+AhC15A8OeSDdLdMyH6EhqDYDmIIm/RmZuFunirTtsyOa3rjwcuSP/40h6OH4JvwloCZXtY8jZ0k2RdUAj0e4dS1h8z12i7VREiMvdPp6Ui2rBsgCsROMiywDn3uHib0ecCg25Rja3CYwIaDttd99TXKxw5rrr6mUCpFJKjZgvqrUXTC9Y2MEDzBZc7iW4XBjZdFsGs7fQh5ceyw8DzVU82Y465/0xmhedamTHzJLfTC3pJ8OsMV6tCdtFaRjJ74w9/WVI/UDz8IGg0/vewgQ92CaDGoqZVguQ+utu0iPVM1JsdwGPKL/jIAeGFNvu3Jq2dym2VNFHvBu1d5CjlrzLCB1U6hEkwZtYcu39Uwr5Zg701IrQqRpsXIdtH1CyUjDLQiWafhypHTlEl8RTz5RgS9eBBO5RX/pQeqD1yUODPXEd3pFf+r8txrlgh3uBxTriWzdoFxiz+g/yS1Bryv1zdI+cdqBlP1zvcMb7k9f88bdXEH7hRk2LvWHqZffkHJ1iw0wxeaTBwzuaaJuu9lPhgt/Y0+TE0P+2VZuKgahyGJ/o/kYkTc+p4NeoxwOgPqQxVyY7FQENODwBGVSCgGW055x1XRpbkuBAKWB7dhRsYrEa3Ix5Dsm4Toepexx8hR/zm/bSYw1oYWGWIDFjdyUqo0D48C6NYPVd604P5wqOUXvi9KHTumFlRdehExDoMqMC/Ow4IfWTPA8ai3rH5GKEwIELcu/NUaHHOav2ENd5QOYCXbc5HReiWupIH651V73ax5A+MhDzTH+ztTCcwQb8qgn4a5/qquVVb5pbMxqNprAQYZdeJSiRqZNKHUYh8vd7/NknAwC1LrMxomFnarCC86qQs6yL4CQcM/fVxmCD7o7o8DIDDbTKsorNcqkgOuwVhA8IfAzmPCiGtuRj640UKh+nJnIWsKzI2JF1XZb0X9QMrR1Zi117y8C0cYJRtpUDzCKUJBHKLjKvsFpzSg/IH1OXZjA3E5YIs+qrKuwJZpWvA1CTKv0JkCh955yIIF/+lJWgQneK9aPG3R0pcJC1oFfbREjXCA20lAHSgkyxNcyVeGBfhJvl6NKA0PLCXKRCgGEzRmTxZ0WxKbkZ8RZhaiz9al//wFjQ10CI5/IPgAlvlAn0uexPV7nxPnu6Qvr4/CG8vPao5BL7BRLGsBXXOxq1q/DLFz7MmtLLQxFg1KXETaGVTcpnWBMrbIlvWUq4BZa/JDbiQtRnqrTAKlQdMaHXtA4Lz29oJozrXONwJbhB6CibrlxbtULtLB2xLeNpCq1+Wo+H8sO7iYkNQ2npecj2ekTddxDuPPpxbdmGNkXscl5igytftBb//P3EbGXuIh0QfqLvuwwZBkr5SoIRCJxbJ4KjhJzHLKtkSlE9mxR+oyThNftafbF2qH1DwcqSpvd9vXDBW93iYC8SxsMc8jpJOZWPiid+bCo/FSVi7I17IB2F5AOGv35TvZL/ZSvz306PP5sc11J3mWd+AdSrtbKEoiInoF6+lXJ+jdgQSVwUEqQoI3IrvDzy1WoGmVeiZKPGSQvoT9zgKriBefFSyd64Qm021Dr28Oxvbn7HC0CEBsstUW6tLKD8hmp0V8wvi/qh+/ygUQZ3vR7WetyKBCxsxt9TUaTlhN33K2fermJZw4HYoMcXEEMogeEIaWcRZnSnXzsqC0pipchPIRsG7ZCuAXfpj2gFOaxo5FFTmxwEl0F83Mie8Dfcwnw9ks6lLxXMAzyiRXHKNqSurNRLGYNdjUYiqWtLV2sboSUhKhkgm+6ul/ie0nnw6Vvzg2zKhODR36P4be3q2DFrq1TOpCINzZ3AM12/nVnzjp8cOYIJ/8eR8/kUk3554/fkrLmoaCs/3yZz95BGOkDt77YOBCq2b9GQublfKDKW5YiAxPGeiDIluSPhLch48bs69sPXyKLCvQNydTfgTWS4vcNHG5vGtdsxka8PxrMfFVRwjy7m/2Zqa+SdTW7OKvulXsnfecrfu9khCozFoy+WmsdQNkd55GQjSIvX+U7NlfHqXIIhKI7NZ6O2T8TKd9ON6dcY+9ndYc8YEqAiMy3aU5/bFn6AD//jDo+0sW0imv0UH0dRLMnvekVzVq3vQ4p0qCBlqX8yO3f0a44z8Ue6/yHPcDPDiZiHe7y+JpWmCyE7ij9InvVBg8MMkSHgif7hmN8AKAMNmz/0obnN3B87zQCnPLKvgf2Rk+S70UELXIHPPilO2jHtiWREDHzUnYM8EKKlAupHckK/UTpDyU+bkTTT1rum/x6+7rFu9Yb8Ma0V4qFZUqAYxn/+Tcdg7Nb9ObjJKaJ90b0tfe1oTY9Hnc1K5P3+wjSWvUOp7ZQMnfEL03I9sSB1cvdSavJ8t60loWAsLY3QefQ/E0kWMnZDvWlkhCI7NnyaS8mTaSD6GV5bH7EXp+uD6/+jYt7KC625GkN6ButYXiVklSMlAw1XtG5gmvQEcmW6WvC12wgT0inQI+F6v889e69pbXzBTyjiMU/Bt2oFJgDzzkqMLAjNCTFVoU6ER6Bmo4L3PJ27lK9LvlRGQ/OYRTuzFiOPug8Pk2klnl3+gVEVEfhVC4GJv/OeSj545fMTWEmjTl0OPzqe/QjJzEBcaFkxRPGmR4PGYh7VLWeOmAAPfwZUOct67nICW/BGjCITKhgMGq6NxjUWb+dUsJ1LbtuSNFAMkXQ1/mrAqmguUxk+Mkz01BQYGw9wHsqn9A5ymZmPY7iiYgv8R8BsT7DZyxaWWVWTS+5/TIUc0psQ8ZKDDTFH9LgAZrpsDFHqxWFO5ojB5javwWTE894djmAQ23lm2awAW6DENvdCdNDDpC5zppwWA1YWHkP4bU4ztI+J8QSWMNnSSbyp9dSAA2iyQk+7YfnO4vAxMB3lI5D4fPX8VlfNgDjvD51kyM0XQEXFxVCi5KkgMe/a3Emtq57YMKIwk+ZlLBLtZhSr6S5Ys+vXPzJIv6abG/nsCnGJLwaDGEHoN+SgUqPlHc0ubj7E37nHzABmDSZPuhi6swYU3ZpNm7iCmEiUg8q5gixp2fdrPQd+xM2abMm6ozot1N6mqIw2kvNSswBPGPMpLtUkrAs2T2nHi25o37pREwsttbThEgn9X0eBfP7P8GKmNFo1akF0RKxpD1QnLCwDCBdWowccyJJUogus8wjBDGzyLp2kc92fPcNQ0BRbOWQVsFEc39dPgL57EzmE5IUb15T2CuOsruKWlZEWSHXCU9KRalvkKHQK2A3Ow2RBTKu1SyQyJv3O3poU5DC5aiMTM829811GrgtYaOqDYXx/h9lDzXkco5uI8fBIygOCKilySoRNkTI7FWRUv93CTjU0fRH+QbQA0bZoRDjsskateU6IbOplLV0mHVMO5/oSbaMy8RaWq+rSO25pFRbE6zZ3NUqrpHwC2II9KmQQMOPUKzey299N+kiwEz0hRsnXFXKC/Zw9CS8nvf78AeZEL2++WV5yMBRGchoBHLoiq4YJOIpHD6JuiJPaOdlIlKv5UI2bDqVllqe1SIwf3twmmzXXHo+LWPkiTz/xunZWItg9qbmEcNEKa7l7MOKc4UGRWULM7vdp4IWpCdGDAPAQq93q2PA6KDBnncVrNfXWQoOWGmgFm/SlsS0JkvsX55r0FmN4otlR32yyiUv+pRtNo8rHYECDXp01hdqM7jM6gJ/g5Uzx/YpB+eWLVtF2nROv57F09xT2PJDv8hiLyHnLQw5sC0/s+zJsOVSjUSIXF0YuTtlwKN6necUkRSZ2KLqduxpe8o2rzFAF2516heLVCSrDF4+BrXTlVCMFX180WsN+djeMItUXMIN2aHbGVDIeYLcOY3ikFrZKkQioQF0zILVfsV3Kvqtu0APhaF9/hwwmRqNkTh7w3sg4h7GOFCK2aaI/pVugpDNgqDB6MfyE9sonJ7nwYuD3Lj4+QA3UuBX5zV+aud8lBewfxbI37G6Qm7zmGNv/MVH/O8uDZs+BA7VtxnG83AvL+toyHCkYntWL0PblQi/QLk0S/My2A51q1BUYYBJYxwkRUJw3RYTrMwg250orWKn08OQqtnZRit5Q+clb6TZl+D+38u0gychMt+G+DMIS20LgWt/rHUm2UtzHU8ldo46xCeDem8fBlH41fk3YdQpLjXLMapJO+g3Mkc1MRmuBg0s82oVVvejNsWN+4AtnYUeUdBhZa8D8YAQuLcPeeQnlE6hlv4pzzEp7jeUrpWUILp/D8Ub2O2MBbRpA2OdXrgUwSScrJCS1B0gyboxiEut0p+LzfXmTBdTB/l+J1FvFQT3AU9Ry+cK+JLeU1/rsg9cKfI2wVaTyX2R1rgDM3fEquiDmhxs3IZHBzJAdadkscXxQd5wq4ukrkbiZCbwx5KN955YziBgUIym6PKorr3S9GHpMvpnNHREUS02OruurmI4opvDpKPOAmR3ZnGrhx3m1rQpT/lC7FJhOHEcvvx/paZFRd+pP70N0ZUr+MLk5lTwyHpiP6gT6Sq/kyyKHL0xkL8SPSg6/HWRwufvim81G0Kmq05Qp9pCVf43JeBt/Xl/bNt6d6PV/frrLmt+8p8L2jRFfkSAuJ3HV7NQo7x0v30xLmQQunRCqZorQkfr9iQQnyGqTyGSMevALbminFoiPY6H5cHE+v9hf6q+YYlQ/+x6FVIEEzkSns+zH+Y/Nz3D6mAIsA+QEcY8+iPajm1qo3JORCsXMpQLHlBgliWUAU8w5rDo9jXuVoJnLYDDGcwSFqGHuObk2mlm94YLkfT0hw289OIUgfJLYN80ChV/G0lQpgScE63A/xXfK1y1nxWW5ZbZmw6zZ2CXWzMlj3TNtPVKSJwIqrBzPPeaWcmIRbyXfA3gKs9ZYcikAgm+jnt8JM12Z2ZemgUqM6Fof4/w169q0mkEn8bzbK1kJhGTH2BaKS3P5pacKF7DNXTfxiWfRLwYEtC6JAlmjNHncuV/68BF+/CAbslC9ADca+H1jidbATPI0MmXbFfatVpBPi9ZwlxCV9w9AFzoM3lqKdVkriz/yl9necLHkLeHWtEKH4YjunX5gWLsxdZvGU/LnzdYy9gUHGMNgjDm12xHn6Qz/r5im9CdNr2DfLnv6Bya7wfo9fm6LcJUVKeTVBNr1v/6+coCdFlP19yGVEM1Si0XfgDQPf7ph15ELzvZxSS0QLvnq9l/vKbaXF3frzteuElhPEcAPNDKg/61dWY8JQyusQhQF7I5N9gx6lBN/PPumfMxugglIsEUll14rwzI4WBsCIyVfVUssROE1GouGsIvQ+4w+VRXHv1EvmLaom6ouwCEoTxUxwSbaDayimtnlU8gyc/dEcjAbJAuK2NoJxXPV0XOMZMH/xYFxJev8MxpI4tqYV3Cj6yFaQVyxfFgD5gfIgQKWh39T6OU8IihBuwLChIeG6asG6aqKdHNOvNNVOrXWczi7kWvNAmd4n0yV9msX16fvW+9TokhOggEFU3ZbX4mMPIQ+vKAJvuKUwGednE2hv4LEiUsEU9QqyIOhxVSUCEyJL55kQjRCg4ZsR+KI2J4w/ykJBI9s2Kz3PD+05hMs4C4jdGwxH517xzpckycKFGpeWwHNYWj8oqlyU4fQWKiOyoW7xfyMyCa5bKIyvKuWQjg1ucXkrO9I0ibKW8GuoAVLI03pRs/W0hO1sT1n0Bp3D791IoyVpuixZGRhRFwZrxs47llwGhq1RmZkvpw3ZWD+Mui7It0HWChCpgOrQQ/CM58JvRklMOFV0N8gNEeO08amtv3mPmFnVBinmCRkJt2UuxxZhWa7nisBe92rWM+UZ/aqgFQqdU88ocmvjJAurjlbi5t44d4vmc1RO8mNKCF/m8F0kmnEajje0s5+Ru7UILHxSEYPJqv2xefTntuLmKD9HOqcDnjzOPwjBx+yxALEOGATfl9N2MMYwtKIE7+4RTn9EP1UWgFULoBqFSdAAQRf5c/Y2UtEF6jvye6haYYddCIjdfuIuDwXkz9ebgezRqTGF2floJA8eUCpsPeAzj0VeeUwN+DeHWlWZzV911gkLLsaYZWeuEGEEKI3NmAF2CdjHcWhsrZjrgLNRjdd8eq7+COpv6F+cmsdSNpmN+tJiHPEwBK+/b9Et2pusPyaVBTjFvwFXcsZ9Vnaim4nDfsCHEUZSmmb13ItpZrZyuzxluA+XSFW09iAQ+t3p9DPEzJP4G0W1tJBK5c1079CtRN+g0gMJhfMRE+zjhTrtVNZTSMDtpWHdbmnM7vtbR+WwgSLh1J2dAYVkzwru0zRgOWIzI9SGQw2gt33n3Z1AWGUFi82p3+hHpWB9ob0S3bbd6h5ID3rNbxgjkfJxCBhSXJoxM4vykj9cQtgYPe0ObSq56phcErHFc/igXnex3Dgf2Ry92oNH0DSUIjYEhlgLYqCnjEW1DkRRyIUl9MIs0lk0PzKOpgDPPkcjcxtIFCLbNl1i185/W0qbztO7PZUojuH+QzdxkHNO3FA2InAS2UZ3gHhUiAyhis/wRc1iek49cSUGSycAUCzD9qzgLJPtv7MwiKOLZ/YWgJRy8ZSyxqWyNexCzHVJbWTY+AbJNX9vi1PcJQo1w0Jq1hVpgTgePPgGS07Ymi7tTXMTDzU3xlLMI1KbV0aDir5mx2ohVgSFW5Lu0u9Vna6ddaievNHml2+SlwscAizNm6N0vPkThM+DvSKhBLrHeLIlgg9++8Qj/Nd+5fLwnCu2ITMXU1WXiFo+74FzYlbcC06IvEmm/XCNFwImLx2cWIb29sY70ExN4EjHydAfvwKYcGy48Kz+jlMW0XxmUkijoyc/lP1tKDlDLgmpqT5XaFnCjdKdqOLggmfjj4OVLoA7yey3sD9rGo21G4M/M3/jbu7GXp5cpOfhYGLhJqqvGze9fwcmhxWlOmgN0UBTrBQs8oXkv2fiwxCViYmb746rgUZvWRLi0Ss4jQpYeiyISCl8nyPNAxluqWyfmDkvXiXtmyqqwnR8bqYAVK346m7ATt85YlBAB755Z7eOShxZXF3+roDAtnGspQ+ygCsAQwn4b00bfGrPot9T3pzAxlLeOcYg9uYNEIitp3xb7HXDuCYCDJQjEXUTY024ZpD0RKsByny2iqwWNzomT7z2zJ2/JJIshh+z94fwDqx2NIWR9IRMx1ZLsGqycczKl7qCOCTd8xMzrKIKW5j6ITweekuOWaJiT6kkwMaKI5SCuCYL3MKE+lOWqyUSs0mML2xZFxg0EsgWkXj8B6/6LONu1J8Gn0KjwnysntT16N44m8/cCG5T6BLzYtUvABP0ful1S3GeqQOsT8gbj5aJcAXGyHpCj5a3M5xHRcXYgpcdRXiKCX6gwCYwpBa/W3fMX0qraGom6f3YbHDIJLY73RAPjlV3kNpq7+ooPQNirjNgWmH81PAdxuxiwZEtUctC4nvKbWh+LbfU9CKbgAFCjygodMDavkUdrvVQBVBdJlUwEZ4CipKf53LpY3AUWVyeXQGMOwwUior1bGAtlOL6ipRE+RqoP9cOhzts6uq+c6ADqnAzWowXqnL7OeFEQg/SWP/1uxNTmh7W8vsv6UYe/AdT9tn5sxW3pThYBtWFibd1uH3HcdOt24+0E2S8zBAnSeHz2EeDavHrAE6aCRI+ZVBeBhbu/Bg7ui9dlEO0kIkC/1VcDkeUv6IkNORIBfu0iZIZYqkk8ZLIPiWZedb8eP2sFBy9V2RR+Auo58hMo+3f+gEoPZTtC+cSPqvXYS4SpZKSykjxCveeC6VEEfkcdzTFMcV4eKKlXWg90WbYWjgU3o/88s9vyNqqGfNqiImLzdhTdrbDewdQTCyq04rrXwQH5mLiD/Y3RFGl8dHLcyRLkFNiyP4DVJjVrT0Ek79EkWqoDbtdAAjjAgO+j5gzngn8mx4kd3DgecbKNr8/o97e5GizcIgIOMyPE3X4kl2r6DHOqnb6RfOvrkRSKmQCs9fRMhAAM7iXgIBjMFM/Tok4syDb/cdQG8WkzguKS++iwmB9IEEXWyMwvvndho3Q6GjJzUiF6/HvXMM0jGJHQdYPhjsptKsRZyLgjYjVjocqUGw0cN2ckGSrfT/PIX4OowoE10EPxHXummj7SH2U1p0rWXXctcNnDTCTMkG04L14Hs+Q6vKtfWrH+KHQrGX+1U6k4S6vW+kXvcwWQ2HHFiHvIvUyqfs2NJU9oR0bK8Hl0xZTvKW+DExGJDd3DKrcAFIDiJifejMJ0AgrQt6cX8qTP1xie9rhEVCtIDawQgoWVTxEoH+vl3JMbVcUpJp+ITqb2dLB+nfrmEFL1OGs2frTP5jyt3dlQMUo6CSRbhLxgUHXQisYJ9YncVU3/ZbX1XP4xPx6mIgWCxXZ2ZXTNXVRBT0wzzDSr9oipsXQUM+gxwpB11y1wSZeRMMDQsCtssW0Kc4T2yEKZrrTx6D5mkXPk5f+cG11x8xDxTo8aRMPentroUJfVCnLbGXfzb1NmOIiuxLev3+9q9hTaR0zfooQdmjQyD8YG8VuLPHUBO0C/sqMSlphoj7/3zJtT2H0tebreHzOMdYbCPZVv0763ZL59tLuqA6QlK6xdYqfiptlWcMoXgCnjmUTqOnKdEzrTQ2nXwbL061tu+k0oDgGfJFi0/GPa5w5lqeZUM0YYz0On/OcY8zjI/FqyR/9nwLB9QK74UIN3a693+n9hnfu/MsF4AwaNqS7cWu6piLLYj8bN12IcnV4tib0zNqDMo/ukjOd/iUKYF4KnSnKO30O07KkCPEZWLWS8dyG91SPJ0je4jqLzphJ6Qd0/8MNxHehWlmYjULTIM5+JQWCHk6Tm18xz7yhmlxFkR3xQmSttKvAENk4OpA5SRb90948mKn2KZ4v9Pj7RQbXtgV2Q9HfO7Zleyq1Z2XyrWOEipn52M2xzsqAEkCxM7C6ln2y0d1mRMJz5pf1LEF5RrZKp76xDXq1e7/Z7W947jahvamfF2DEMpiDHAbf5ehkbjR9DOKQggGL0hbcOsePGDJ4cijhMSc+iHin5FjfU9Ta0BssuVXSh5o8FuWsmZLNJ0t4kcvwBfWKV81i86Ysx8A+iBmIswOOlF413p0w7/a96qs59BYzkEu7B1r/wbpO7AIEBzVhKkr9IEEYhGYavUaA0MqtM+XlfisxsHr6hDbEiYWltO87EcXKvc32UT5L24ynGPxL9SlW3tLP8HnIfnU41pzBs1dloYQZAVQ1Z8l9cFP47xfjbuO6PM8rmSPWIq9N+kohpXW0H67mCcp8i8zG32hisXZJHmWxsSK/D3dQNpmCMzJJOdAUqN7Axw/U2ehgxs+c0pLwDXDxmztIz2bSJ2XAoo9kUR0Tuv8e7UlO/DAJVlw3HCYJNORVzQMx4LC5LP6+oJOdbc4ZsvC5+muMzsXthTodaWIiNvLFkWQ3VPlj1SZQtMVQ1ZKLsBpiIYMxBkqM6NIOWgnplNAeyqmwAaEPzrnjnlvNaqOXL2PhcFi5BBjHNh7kimacUecUr6lVlhefM6ouqJ/6zh+mHcz5nsWM3Wdt2aQAYXYYK8/LiHPT84PhlX/wu5C2hxa8zV9Rkm/gguRCJu6jxv/OYvyI7SDvZARp2RRhbtytALE0XEDaO07jaG5e7JqU+v9cWcMJeZzCyJYtrr/KugJcPB8xxu398puUBD+vNAGr3gCfTaFNOOkRpuo2NkPk6XlKGMLkXo31huAbyYMWJL5G4Lf3QWz0mNJ3j3f0YkmR+5mm7Qw3KESWRhLVIG5dcZKI2GqxwkvZUJdXd3N9GLabm7+tAbRt+x/iMkmxQYdoYIuwTHDNdw4KppupPOW66tVWH2hmnaRbJYV8aORaxj+Q9dt+5+SaagDWL3bKOk+zbrrRAGx8oNyPEopSLKl9SABwkTNHbKJCQsqQZUL/qyY4RPacXnZ+6znMBVlh7ymaHUi3RRTAI3jHw5Vt0MXZtuFu0NKU6bRsAa6wl30R/QvgtKQMxeh34QMBHOT4ZJ+k2bCvLkWLx1s6EMIs3uyBq3padXMBM1bhUZuIC4KDh+G2Oct+59ZybqO45mzL3HgVAEuFvFDcHxu6GJE9kW5tpQdHHNzRwBuvH4DbyzgMH0xsOwKfRzhDTFEIFn11CPMY9oALvXMHv/BYGjSCJ+XE2acVla1APdSezGugeEXPZP/r6NcHCaW5hTvYF5KU5AhfICNy17kS83MIKgrdi/L83YfPrDvxh/ig0i3BBYex1i59zyedcNCDylWxelsBpxcCQNG8TXNGdLOVYE65Psi8l/7LGxbyGjMtjPSIRyN10i74XLhIt0MAYNUg8m3cWSN2QO1DcLu4Apzykz1vhtpfEuqNgV37PFr7bOpumi0FJIk13SefUj0rcmIXdvRHO+pg+DzyEBQ2a34IHT/nnK5qALvua7k7P8F9NBDVRD5I2x+9ItMwVC5IxWkQiqPrxp4dkfxNc0JcFE/l4qU5LXH1a4F1oueTV6BGgt+zWolC+5jOQfGTj+EPyCXEv/nJZnTfGMvxpIfBvN8vKIF1MlYztBb7xyfLFf8wHHXio44lPURb73JjZQEe7eYiqImoIWV83smukbxAiuU3gw1+tv7v6WN++wnrO3ONbJ3gvAqxgmqRFisLzpOrf2EeiteaDydzhYply8c4k7e67VE1pem75auIQn48gI/UI1MvEhb0nLxf1BqB5PYAyEEJGqT+ZN1uRl0bK4gs6DP+cxOThDbihh5O9e5u170JrP2FttmNe3qg3jlN8fpxM2rRd3Xkh2aQAUh8B6Ia7Lt4Dc7Dl6hCb0BWoTQTWRwj8utTWYnRn3r/m2v/yCRysDLlsQnpMFFKz/4W1hElxm1IFXZGkeqyBU6dgJOYZPNCmWUjKXpHoLQGHJlTadbwh9FdWA+SBaGprpc/coztOTFG+AyZ64Yc0M7G7CZGi0KnjpiHU96PyeIkWVkBe2GtltPi9KS316HF2VgonT9Q9Yaa/cdNG9uzuDuY4KxYH2KNvWw+9Bfyc6OwFPaVFYoVR+R068qlWb6GNXLHAQMA1Suvy0hcjX6FdMGOMXZVEkiYbX/JickHuRh7DP4twUFONWVJ/dIvc0FDWs3BSt8hGMf1q60vC+K6JH3QCEYDvkjjbUU0WfrQDTmgC9YrfLujtHIFJjU1JTYB4iVLbOOE+7vVxUwgRng0gRFDmOCajb42HX91z7dYmeoxfV1GmglTXJFa7JnGMVX41t0af/XXmt26H2W/+acTtnZYlMM/mhW1F6Qbv5KwrRf8zalowxyeH4CgnKpk3XlMI14h5enfOHb07s1U3s68WNB2921/YKRN9Jd93Yqcir/McUhtnnnGaK4ISYNZqRvhqv+OxbJf1twH+mijKY7jXkmvOezHNQ1c5rgIJ6i5URMBNggaQN7iuPdzAYW4feEI2pvvpE1fO2TZxGEoH87m32ExYjhPKWQt9C6Y30kgoejf27x4COfOFaswYhHLo+ES5u9afrNfoDei4JxxZ/8l7lN0HB9j4TtZHSY0o8ogMWVQQItF7itnUKqgG8RFTiCcj4hkjS5KJbI4bra59R9t3fB0iUZI/vA05etDBdY92yovN3Q+ZmrnEYFL5bRN6j6guy2n0yK37ePqbIUkT1iA5+JxhiegsmzaxuLullaNjVPVti1i0f9LBpE0iuEFezCKZ4EX93UrTv485oFH98WCnNaPgisKpBcO4T13PHTQtQmb5oXjCrDipVCtHkRLU7z79SYn39Mb1J46bOuDE4beIW7Sn5uUPopq6jBu6eobstPzXnzQ5nVdqvQoN/kEwydxvs6yEJufXVtTNrlVAtLUc9QVEMHRBnaA8Vlr+ZIPA2qL/ekG07IH0eL1U1QKcU+iagt8Szt1IO1BApI4RoDlKfOHAn4rSlhPSZjJSBFIS+LHKYSDzSRWW1Ay8Lw/XZmgldkAffTLdRksTgrfomvtC0n+XJKx0PUyh8mcLO8JBXUfKYcrcP6ykATPw4AxPLBWwlLkaMweEoAZgeFhu7SWJYnj1S9f/KxIGivxgmwNWzyKd3BTcPW64k88foGNKAfv37FaXjs2PwL/wJGDzb1D33OgCN33+4EbUG87OscxyO1Jb7YrV9uWfuAbEm1Q4wEPirp1NK7BNnkSIEocXX/x4sgfhecCKZN7qd3n8FcdSffy4N8QEytFTdO8OK19LnBa7wFIsnQI5U9QMDPxwazg022vt+BPZcoLBz9VgK/WtGvcfeB+CdMpG3exrI0ItBQxzqY2DBt6UomQw7XMjWS+zKvPI5wanVBaz9Cd5b5Knv0zyBzxVV437um9kCQ/Zzl++sicoER/1L5UHYHjoKZoMt6rp00mBFpBpOW4c2I+E/g82OZV9q6xzN3VqXlOyoBELKoFEEj+i3E5Nna1V7ywCDtREbNLtKQ8IC5nXenbgKAxYZsZS7Oine4t+HIU5YQU9hgTKgEoTYEeqTv5JJBSc9arpEk2lQG15ALpBRwdnNXsE1xD8qQvtkp84l2XZl2ojFiduR04e1BIbe+D0Hvi2VGyaEpnCdo=\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_end","contentIndex":1,"toolCall":{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|T2qbJiuPDWPdOJPXxr7DJxW2UnbCNXkKMXpvi+nhZPWZmlFam7X73LXJdCZS1QscxN+KfxYl0kNgU/vdWLW8b26UGcgW9Wv+WZRxB+6SGvqcoJEYTW/gOzlkP5jNZbIdjtJJlClkZIPxrZDw72zqQeDFiMFvr0mWwchOK4+72sfCOBK0ZG3DLNucbT9CKDId32wg+BDbm7mi8SAS++u2yw/6ttd3Qe78PRVrKA1UW1KthNX7g7+rJTMqR4qz3QfTSB8ChtpAbsu2ZznGVii2WQS+9VGwYf4Mk/VEzBFl5l0MH67yKfLqGMSBSJCqN0MD0/OUnCJJJDKBCpEBK43FjVMp+ommB7g+HHIew6WuVRACz51o397MpCkY4uFc3Gc0TnpT+T7DoZW4zENeSthGitmkHWGQuIY=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md"}},"partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775940989982,"responseId":"vVXbWuTsurcCEH3lebCLSMTpH29lOzzUVk3g2U/tSH5IjZDrqPuGJz/028QZKSHrAu4dgXCfJ9JJiDOrHOpqy8xlm/lY3hrhsdvBghu3ZpWUUtV1sHtMXld1lLP5qU4S3z3C7Ob6TFVdAFfInJ8GZnMfa4lNzzv6AN1CanoRyvC2IYKHtIMWkW4Wj1y4gt5lEXDEdbW7MlC+VJRANbKs2nmKU1Yw9ttrn1PyXxodx68AGl1WQdtC8XFYK9Uj0pTiz50BeS8Dy1tGiBAkOu4iNGZfRafZP1QwncOnPsozZFhvcZHCG+OUPVd/SEMPIs45OXV94t+xE1+KUXtJMLWH3GXLmYbHaNXnAxk6tFgJU8NAbRZkzqWdijxxhvubBapmaUFEa6Ln+s5wHhubiTMMUZiCAUbRv5XdBg=="}}
{"type":"message_end","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":5863,"output":1350,"cacheRead":2560,"cacheWrite":0,"totalTokens":9773,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775940989982,"responseId":"FL+gsVT1reycKksFn6MwxU8Zt1kXl8RfSVR2kbr3nHKtHqyh9cymZonkzEgQAwjBAhxpUUBmTK1VWhm2O35JUktvx17GredWRNNgOqwNlFXDHiZQCOC06P+mxMLpqwhRjfD/mFjygllGs5G5wPl6ItILzJH4l5eTta685AD//vdB/tZxpuJl9/hnDlfQFXE8PROxIVY7zjCVZlsZkwGPgJgEAjGLnZGv8utqsrP4pBiBni67MotFnEW4lsFB+zlt9T5EkbNqg+QQjA1b+GLr3wHY5i3ykCvN6O3m8iiKCHRoQ5o6tgJPE0wYRTnfx62nx1+BQxf6llIBQS1gyPT3oglAiiZKCw6q2qUlqJDcNU4edrx3DAAdciWB1KD8gVlch9c9jNLAJI2ekPUjmTaEaLh0jwzYpRbZzw=="}}
{"type":"tool_execution_start","toolCallId":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","toolName":"read","args":{"path":"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md"}}
{"type":"tool_execution_end","toolCallId":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","toolName":"read","result":{"content":[{"type":"text","text":"---\nname: extract\ndescription: Extract and consolidate reusable components, design tokens, and patterns into your design system. Identifies opportunities for systematic reuse and enriches your component library. Use when the user asks to create components, refactor repeated UI patterns, build a design system, or extract tokens.\n---\n\nIdentify reusable patterns, components, and design tokens, then extract and consolidate them into the design system for systematic reuse.\n\n## Discover\n\nAnalyze the target area to identify extraction opportunities:\n\n1. **Find the design system**: Locate your design system, component library, or shared UI directory (grep for \"design system\", \"ui\", \"components\", etc.). Understand its structure:\n   - Component organization and naming conventions\n   - Design token structure (if any)\n   - Documentation patterns\n   - Import/export conventions\n   \n   **CRITICAL**: If no design system exists, ask before creating one. Understand the preferred location and structure first.\n\n2. **Identify patterns**: Look for:\n   - **Repeated components**: Similar UI patterns used multiple times (buttons, cards, inputs, etc.)\n   - **Hard-coded values**: Colors, spacing, typography, shadows that should be tokens\n   - **Inconsistent variations**: Multiple implementations of the same concept (3 different button styles)\n   - **Reusable patterns**: Layout patterns, composition patterns, interaction patterns worth systematizing\n\n3. **Assess value**: Not everything should be extracted. Consider:\n   - Is this used 3+ times, or likely to be reused?\n   - Would systematizing this improve consistency?\n   - Is this a general pattern or context-specific?\n   - What's the maintenance cost vs benefit?\n\n## Plan Extraction\n\nCreate a systematic extraction plan:\n\n- **Components to extract**: Which UI elements become reusable components?\n- **Tokens to create**: Which hard-coded values become design tokens?\n- **Variants to support**: What variations does each component need?\n- **Naming conventions**: Component names, token names, prop names that match existing patterns\n- **Migration path**: How to refactor existing uses to consume the new shared versions\n\n**IMPORTANT**: Design systems grow incrementally. Extract what's clearly reusable now, not everything that might someday be reusable.\n\n## Extract & Enrich\n\nBuild improved, reusable versions:\n\n- **Components**: Create well-designed components with:\n  - Clear props API with sensible defaults\n  - Proper variants for different use cases\n  - Accessibility built in (ARIA, keyboard navigation, focus management)\n  - Documentation and usage examples\n  \n- **Design tokens**: Create tokens with:\n  - Clear naming (primitive vs semantic)\n  - Proper hierarchy and organization\n  - Documentation of when to use each token\n  \n- **Patterns**: Document patterns with:\n  - When to use this pattern\n  - Code examples\n  - Variations and combinations\n\n**NEVER**:\n- Extract one-off, context-specific implementations without generalization\n- Create components so generic they're useless\n- Extract without considering existing design system conventions\n- Skip proper TypeScript types or prop documentation\n- Create tokens for every single value (tokens should have semantic meaning)\n\n## Migrate\n\nReplace existing uses with the new shared versions:\n\n- **Find all instances**: Search for the patterns you've extracted\n- **Replace systematically**: Update each use to consume the shared version\n- **Test thoroughly**: Ensure visual and functional parity\n- **Delete dead code**: Remove the old implementations\n\n## Document\n\nUpdate design system documentation:\n\n- Add new components to the component library\n- Document token usage and values\n- Add examples and guidelines\n- Update any Storybook or component catalog\n\nRemember: A good design system is a living system. Extract patterns as they emerge, enrich them thoughtfully, and maintain them consistently."}]},"isError":false}
{"type":"message_start","message":{"role":"toolResult","toolCallId":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","toolName":"read","content":[{"type":"text","text":"---\nname: extract\ndescription: Extract and consolidate reusable components, design tokens, and patterns into your design system. Identifies opportunities for systematic reuse and enriches your component library. Use when the user asks to create components, refactor repeated UI patterns, build a design system, or extract tokens.\n---\n\nIdentify reusable patterns, components, and design tokens, then extract and consolidate them into the design system for systematic reuse.\n\n## Discover\n\nAnalyze the target area to identify extraction opportunities:\n\n1. **Find the design system**: Locate your design system, component library, or shared UI directory (grep for \"design system\", \"ui\", \"components\", etc.). Understand its structure:\n   - Component organization and naming conventions\n   - Design token structure (if any)\n   - Documentation patterns\n   - Import/export conventions\n   \n   **CRITICAL**: If no design system exists, ask before creating one. Understand the preferred location and structure first.\n\n2. **Identify patterns**: Look for:\n   - **Repeated components**: Similar UI patterns used multiple times (buttons, cards, inputs, etc.)\n   - **Hard-coded values**: Colors, spacing, typography, shadows that should be tokens\n   - **Inconsistent variations**: Multiple implementations of the same concept (3 different button styles)\n   - **Reusable patterns**: Layout patterns, composition patterns, interaction patterns worth systematizing\n\n3. **Assess value**: Not everything should be extracted. Consider:\n   - Is this used 3+ times, or likely to be reused?\n   - Would systematizing this improve consistency?\n   - Is this a general pattern or context-specific?\n   - What's the maintenance cost vs benefit?\n\n## Plan Extraction\n\nCreate a systematic extraction plan:\n\n- **Components to extract**: Which UI elements become reusable components?\n- **Tokens to create**: Which hard-coded values become design tokens?\n- **Variants to support**: What variations does each component need?\n- **Naming conventions**: Component names, token names, prop names that match existing patterns\n- **Migration path**: How to refactor existing uses to consume the new shared versions\n\n**IMPORTANT**: Design systems grow incrementally. Extract what's clearly reusable now, not everything that might someday be reusable.\n\n## Extract & Enrich\n\nBuild improved, reusable versions:\n\n- **Components**: Create well-designed components with:\n  - Clear props API with sensible defaults\n  - Proper variants for different use cases\n  - Accessibility built in (ARIA, keyboard navigation, focus management)\n  - Documentation and usage examples\n  \n- **Design tokens**: Create tokens with:\n  - Clear naming (primitive vs semantic)\n  - Proper hierarchy and organization\n  - Documentation of when to use each token\n  \n- **Patterns**: Document patterns with:\n  - When to use this pattern\n  - Code examples\n  - Variations and combinations\n\n**NEVER**:\n- Extract one-off, context-specific implementations without generalization\n- Create components so generic they're useless\n- Extract without considering existing design system conventions\n- Skip proper TypeScript types or prop documentation\n- Create tokens for every single value (tokens should have semantic meaning)\n\n## Migrate\n\nReplace existing uses with the new shared versions:\n\n- **Find all instances**: Search for the patterns you've extracted\n- **Replace systematically**: Update each use to consume the shared version\n- **Test thoroughly**: Ensure visual and functional parity\n- **Delete dead code**: Remove the old implementations\n\n## Document\n\nUpdate design system documentation:\n\n- Add new components to the component library\n- Document token usage and values\n- Add examples and guidelines\n- Update any Storybook or component catalog\n\nRemember: A good design system is a living system. Extract patterns as they emerge, enrich them thoughtfully, and maintain them consistently."}],"isError":false,"timestamp":1775941019894}}
{"type":"message_end","message":{"role":"toolResult","toolCallId":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","toolName":"read","content":[{"type":"text","text":"---\nname: extract\ndescription: Extract and consolidate reusable components, design tokens, and patterns into your design system. Identifies opportunities for systematic reuse and enriches your component library. Use when the user asks to create components, refactor repeated UI patterns, build a design system, or extract tokens.\n---\n\nIdentify reusable patterns, components, and design tokens, then extract and consolidate them into the design system for systematic reuse.\n\n## Discover\n\nAnalyze the target area to identify extraction opportunities:\n\n1. **Find the design system**: Locate your design system, component library, or shared UI directory (grep for \"design system\", \"ui\", \"components\", etc.). Understand its structure:\n   - Component organization and naming conventions\n   - Design token structure (if any)\n   - Documentation patterns\n   - Import/export conventions\n   \n   **CRITICAL**: If no design system exists, ask before creating one. Understand the preferred location and structure first.\n\n2. **Identify patterns**: Look for:\n   - **Repeated components**: Similar UI patterns used multiple times (buttons, cards, inputs, etc.)\n   - **Hard-coded values**: Colors, spacing, typography, shadows that should be tokens\n   - **Inconsistent variations**: Multiple implementations of the same concept (3 different button styles)\n   - **Reusable patterns**: Layout patterns, composition patterns, interaction patterns worth systematizing\n\n3. **Assess value**: Not everything should be extracted. Consider:\n   - Is this used 3+ times, or likely to be reused?\n   - Would systematizing this improve consistency?\n   - Is this a general pattern or context-specific?\n   - What's the maintenance cost vs benefit?\n\n## Plan Extraction\n\nCreate a systematic extraction plan:\n\n- **Components to extract**: Which UI elements become reusable components?\n- **Tokens to create**: Which hard-coded values become design tokens?\n- **Variants to support**: What variations does each component need?\n- **Naming conventions**: Component names, token names, prop names that match existing patterns\n- **Migration path**: How to refactor existing uses to consume the new shared versions\n\n**IMPORTANT**: Design systems grow incrementally. Extract what's clearly reusable now, not everything that might someday be reusable.\n\n## Extract & Enrich\n\nBuild improved, reusable versions:\n\n- **Components**: Create well-designed components with:\n  - Clear props API with sensible defaults\n  - Proper variants for different use cases\n  - Accessibility built in (ARIA, keyboard navigation, focus management)\n  - Documentation and usage examples\n  \n- **Design tokens**: Create tokens with:\n  - Clear naming (primitive vs semantic)\n  - Proper hierarchy and organization\n  - Documentation of when to use each token\n  \n- **Patterns**: Document patterns with:\n  - When to use this pattern\n  - Code examples\n  - Variations and combinations\n\n**NEVER**:\n- Extract one-off, context-specific implementations without generalization\n- Create components so generic they're useless\n- Extract without considering existing design system conventions\n- Skip proper TypeScript types or prop documentation\n- Create tokens for every single value (tokens should have semantic meaning)\n\n## Migrate\n\nReplace existing uses with the new shared versions:\n\n- **Find all instances**: Search for the patterns you've extracted\n- **Replace systematically**: Update each use to consume the shared version\n- **Test thoroughly**: Ensure visual and functional parity\n- **Delete dead code**: Remove the old implementations\n\n## Document\n\nUpdate design system documentation:\n\n- Add new components to the component library\n- Document token usage and values\n- Add examples and guidelines\n- Update any Storybook or component catalog\n\nRemember: A good design system is a living system. Extract patterns as they emerge, enrich them thoughtfully, and maintain them consistently."}],"isError":false,"timestamp":1775941019894}}
{"type":"turn_end","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":5863,"output":1350,"cacheRead":2560,"cacheWrite":0,"totalTokens":9773,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775940989982,"responseId":"FL+gsVT1reycKksFn6MwxU8Zt1kXl8RfSVR2kbr3nHKtHqyh9cymZonkzEgQAwjBAhxpUUBmTK1VWhm2O35JUktvx17GredWRNNgOqwNlFXDHiZQCOC06P+mxMLpqwhRjfD/mFjygllGs5G5wPl6ItILzJH4l5eTta685AD//vdB/tZxpuJl9/hnDlfQFXE8PROxIVY7zjCVZlsZkwGPgJgEAjGLnZGv8utqsrP4pBiBni67MotFnEW4lsFB+zlt9T5EkbNqg+QQjA1b+GLr3wHY5i3ykCvN6O3m8iiKCHRoQ5o6tgJPE0wYRTnfx62nx1+BQxf6llIBQS1gyPT3oglAiiZKCw6q2qUlqJDcNU4edrx3DAAdciWB1KD8gVlch9c9jNLAJI2ekPUjmTaEaLh0jwzYpRbZzw=="},"toolResults":[{"role":"toolResult","toolCallId":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","toolName":"read","content":[{"type":"text","text":"---\nname: extract\ndescription: Extract and consolidate reusable components, design tokens, and patterns into your design system. Identifies opportunities for systematic reuse and enriches your component library. Use when the user asks to create components, refactor repeated UI patterns, build a design system, or extract tokens.\n---\n\nIdentify reusable patterns, components, and design tokens, then extract and consolidate them into the design system for systematic reuse.\n\n## Discover\n\nAnalyze the target area to identify extraction opportunities:\n\n1. **Find the design system**: Locate your design system, component library, or shared UI directory (grep for \"design system\", \"ui\", \"components\", etc.). Understand its structure:\n   - Component organization and naming conventions\n   - Design token structure (if any)\n   - Documentation patterns\n   - Import/export conventions\n   \n   **CRITICAL**: If no design system exists, ask before creating one. Understand the preferred location and structure first.\n\n2. **Identify patterns**: Look for:\n   - **Repeated components**: Similar UI patterns used multiple times (buttons, cards, inputs, etc.)\n   - **Hard-coded values**: Colors, spacing, typography, shadows that should be tokens\n   - **Inconsistent variations**: Multiple implementations of the same concept (3 different button styles)\n   - **Reusable patterns**: Layout patterns, composition patterns, interaction patterns worth systematizing\n\n3. **Assess value**: Not everything should be extracted. Consider:\n   - Is this used 3+ times, or likely to be reused?\n   - Would systematizing this improve consistency?\n   - Is this a general pattern or context-specific?\n   - What's the maintenance cost vs benefit?\n\n## Plan Extraction\n\nCreate a systematic extraction plan:\n\n- **Components to extract**: Which UI elements become reusable components?\n- **Tokens to create**: Which hard-coded values become design tokens?\n- **Variants to support**: What variations does each component need?\n- **Naming conventions**: Component names, token names, prop names that match existing patterns\n- **Migration path**: How to refactor existing uses to consume the new shared versions\n\n**IMPORTANT**: Design systems grow incrementally. Extract what's clearly reusable now, not everything that might someday be reusable.\n\n## Extract & Enrich\n\nBuild improved, reusable versions:\n\n- **Components**: Create well-designed components with:\n  - Clear props API with sensible defaults\n  - Proper variants for different use cases\n  - Accessibility built in (ARIA, keyboard navigation, focus management)\n  - Documentation and usage examples\n  \n- **Design tokens**: Create tokens with:\n  - Clear naming (primitive vs semantic)\n  - Proper hierarchy and organization\n  - Documentation of when to use each token\n  \n- **Patterns**: Document patterns with:\n  - When to use this pattern\n  - Code examples\n  - Variations and combinations\n\n**NEVER**:\n- Extract one-off, context-specific implementations without generalization\n- Create components so generic they're useless\n- Extract without considering existing design system conventions\n- Skip proper TypeScript types or prop documentation\n- Create tokens for every single value (tokens should have semantic meaning)\n\n## Migrate\n\nReplace existing uses with the new shared versions:\n\n- **Find all instances**: Search for the patterns you've extracted\n- **Replace systematically**: Update each use to consume the shared version\n- **Test thoroughly**: Ensure visual and functional parity\n- **Delete dead code**: Remove the old implementations\n\n## Document\n\nUpdate design system documentation:\n\n- Add new components to the component library\n- Document token usage and values\n- Add examples and guidelines\n- Update any Storybook or component catalog\n\nRemember: A good design system is a living system. Extract patterns as they emerge, enrich them thoughtfully, and maintain them consistently."}],"isError":false,"timestamp":1775941019894}]}
{"type":"turn_start"}
{"type":"message_start","message":{"role":"assistant","content":[],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_start","contentIndex":0,"partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{},"partialJson":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{},"partialJson":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"{\"","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"path","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{},"partialJson":"{\"path"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{},"partialJson":"{\"path"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"\":","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{},"partialJson":"{\"path\":"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{},"partialJson":"{\"path\":"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"\"/","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/"},"partialJson":"{\"path\":\"/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/"},"partialJson":"{\"path\":\"/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"home","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home"},"partialJson":"{\"path\":\"/home"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home"},"partialJson":"{\"path\":\"/home"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/al","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/al"},"partialJson":"{\"path\":\"/home/al"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/al"},"partialJson":"{\"path\":\"/home/al"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"ex","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex"},"partialJson":"{\"path\":\"/home/alex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex"},"partialJson":"{\"path\":\"/home/alex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/.","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/."},"partialJson":"{\"path\":\"/home/alex/."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/."},"partialJson":"{\"path\":\"/home/alex/."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"pi","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi"},"partialJson":"{\"path\":\"/home/alex/.pi"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi"},"partialJson":"{\"path\":\"/home/alex/.pi"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/"},"partialJson":"{\"path\":\"/home/alex/.pi/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/"},"partialJson":"{\"path\":\"/home/alex/.pi/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"agent","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent"},"partialJson":"{\"path\":\"/home/alex/.pi/agent"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent"},"partialJson":"{\"path\":\"/home/alex/.pi/agent"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"skills","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/frontend","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"normalize","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/S","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize/S"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/normalize/S"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize/S"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/normalize/S"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"K","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize/SK"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/normalize/SK"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize/SK"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/normalize/SK"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"ILL","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize/SKILL"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/normalize/SKILL"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize/SKILL"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/normalize/SKILL"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":".md","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"\"}","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_end","contentIndex":0,"toolCall":{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|f4UDUquG6z0nnzdYLsE0p5C7pD7jfEluHWo8s5hql3cMonfJhvHLhh7D3FKQ7CPHoiKpu8clGmBYRCrUagIZo5lkCjI1HlX05U2F54MIDkJ+VmT94nFe7qp7zOKaQWtTs7kWne67All9k6fOQMb0afnEOiGOTNG/AwA5sjSjgwvLNrvHb5KCtr3K21GhJ+g9+iIKnBz2i1AxsAQ2hM6OWjgtL+Xhxd+DH7lB282atNrL2Ki5Ks8INSKo74iO6luXvN1XZsjYJouQC8QTZ1gepCUMS0R6iY5pTAKRpieReKdxykt7cZH00SDjJsqtszqT2X6hGiZ0dfKEtfZfc3cWVPSrTE73YHbqyEy4V0P6lTSp48D9htEmlJoy4w+GIK/lW/KkjVCh4l1MiDo29NjoSFT6D5R4VsU=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md"}},"partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941019899,"responseId":"yOrs4FaGfP9PrynKA5NdCAmEV3Hqxt+7fg8y25Jh1D/9zmeEtLJWNLKn9j5d/9iyUjVArj2lV8L7xhWiPpPXd3r+lyn3uYLHFhxg5WMo0s5dbB3e+kS9elcJCD63xR9lZgejm6UklJAJwFOrve+H/EPQyd7iRm+dFThzBkP/PIs1fQ6Hwm6RhEU/7DxVA6XYM+YzKU7rIpy6fmvFPRMOxblf12ZZDW3dA07pse5gOXHr6zqNtNIIzm1U8k+iyi8iu6na9FcM1Prd4/3SGQOIqwkX5A9XHY7vPxv/tjEaOuyss92kzqSqlAF80Ptb1NlUj639kfyATKbSmotswUD45tfoRd4do3zpG9lHkYmOVDqmQA1Pkqi8wINrmOl67Zw0L7FRUYe9cV4CbVFwHSfpWRbIcrRDBpy7Bw=="}}
{"type":"message_end","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":7995,"output":33,"cacheRead":2560,"cacheWrite":0,"totalTokens":10588,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775941019899,"responseId":"PwPfxd9e/VNsG8BnFxFvR7HluyCziWk8/gAoxe40kwsL57dW6hGmABzppW+jZMo81nsU7LituNndEhg/2X6Uv+cRTlFe87yrlCVadJmex8EheMklL9NeYnlNCljyrL70QYFsTADa7RPWrlBqJl0eQOOfDAoR9shdmBrdkx/I3qWx7CPFO5vPaCM/kvKSTIRoCspg45x+gWfmdR1DIyp25R/cRxdqNYjyfdJt+sPDLTJbOX/uWenT/fSXmRsVl4qhpItfkvDG2oQtCbXs5X9i713/IDDozcABqC/vGymiVDv22PVmMZlcatxoQ9jBakNQO4SKJhG4Mob3CXEfs0gKQI4f3ocrrnSbttLOeZ+dk5+PcqXoIEgBE9v5EjUrtq1XZMNZ3pjQNoSxIta2EAxnI/5Z8T47cEJGeg=="}}
{"type":"tool_execution_start","toolCallId":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","toolName":"read","args":{"path":"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md"}}
{"type":"tool_execution_end","toolCallId":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","toolName":"read","result":{"content":[{"type":"text","text":"---\nname: normalize\ndescription: Audits and realigns UI to match design system standards, spacing, tokens, and patterns. Use when the user mentions consistency, design drift, mismatched styles, tokens, or wants to bring a feature back in line with the system.\n---\n\nAnalyze and redesign the feature to perfectly match our design system standards, aesthetics, and established patterns.\n\n## MANDATORY PREPARATION\n\nInvoke /frontend-design — it contains design principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first.\n\n---\n\n## Plan\n\nBefore making changes, deeply understand the context:\n\n1. **Discover the design system**: Search for design system documentation, UI guidelines, component libraries, or style guides (grep for \"design system\", \"ui guide\", \"style guide\", etc.). Study it thoroughly until you understand:\n   - Core design principles and aesthetic direction\n   - Target audience and personas\n   - Component patterns and conventions\n   - Design tokens (colors, typography, spacing)\n   \n   **CRITICAL**: If something isn't clear, ask. Don't guess at design system principles.\n\n2. **Analyze the current feature**: Assess what works and what doesn't:\n   - Where does it deviate from design system patterns?\n   - Which inconsistencies are cosmetic vs. functional?\n   - What's the root cause—missing tokens, one-off implementations, or conceptual misalignment?\n\n3. **Create a normalization plan**: Define specific changes that will align the feature with the design system:\n   - Which components can be replaced with design system equivalents?\n   - Which styles need to use design tokens instead of hard-coded values?\n   - How can UX patterns match established user flows?\n   \n   **IMPORTANT**: Great design is effective design. Prioritize UX consistency and usability over visual polish alone. Think through the best possible experience for your use case and personas first.\n\n## Execute\n\nSystematically address all inconsistencies across these dimensions:\n\n- **Typography**: Use design system fonts, sizes, weights, and line heights. Replace hard-coded values with typographic tokens or classes.\n- **Color & Theme**: Apply design system color tokens. Remove one-off color choices that break the palette.\n- **Spacing & Layout**: Use spacing tokens (margins, padding, gaps). Align with grid systems and layout patterns used elsewhere.\n- **Components**: Replace custom implementations with design system components. Ensure props and variants match established patterns.\n- **Motion & Interaction**: Match animation timing, easing, and interaction patterns to other features.\n- **Responsive Behavior**: Ensure breakpoints and responsive patterns align with design system standards.\n- **Accessibility**: Verify contrast ratios, focus states, ARIA labels match design system requirements.\n- **Progressive Disclosure**: Match information hierarchy and complexity management to established patterns.\n\n**NEVER**:\n- Create new one-off components when design system equivalents exist\n- Hard-code values that should use design tokens\n- Introduce new patterns that diverge from the design system\n- Compromise accessibility for visual consistency\n\nThis is not an exhaustive list—apply judgment to identify all areas needing normalization.\n\n## Clean Up\n\nAfter normalization, ensure code quality:\n\n- **Consolidate reusable components**: If you created new components that should be shared, move them to the design system or shared UI component path.\n- **Remove orphaned code**: Delete unused implementations, styles, or files made obsolete by normalization.\n- **Verify quality**: Lint, type-check, and test according to repository guidelines. Ensure normalization didn't introduce regressions.\n- **Ensure DRYness**: Look for duplication introduced during refactoring and consolidate.\n\nRemember: You are a brilliant frontend designer with impeccable taste, equally strong in UX and UI. Your attention to detail and eye for end-to-end user experience is world class. Execute with precision and thoroughness."}]},"isError":false}
{"type":"message_start","message":{"role":"toolResult","toolCallId":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","toolName":"read","content":[{"type":"text","text":"---\nname: normalize\ndescription: Audits and realigns UI to match design system standards, spacing, tokens, and patterns. Use when the user mentions consistency, design drift, mismatched styles, tokens, or wants to bring a feature back in line with the system.\n---\n\nAnalyze and redesign the feature to perfectly match our design system standards, aesthetics, and established patterns.\n\n## MANDATORY PREPARATION\n\nInvoke /frontend-design — it contains design principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first.\n\n---\n\n## Plan\n\nBefore making changes, deeply understand the context:\n\n1. **Discover the design system**: Search for design system documentation, UI guidelines, component libraries, or style guides (grep for \"design system\", \"ui guide\", \"style guide\", etc.). Study it thoroughly until you understand:\n   - Core design principles and aesthetic direction\n   - Target audience and personas\n   - Component patterns and conventions\n   - Design tokens (colors, typography, spacing)\n   \n   **CRITICAL**: If something isn't clear, ask. Don't guess at design system principles.\n\n2. **Analyze the current feature**: Assess what works and what doesn't:\n   - Where does it deviate from design system patterns?\n   - Which inconsistencies are cosmetic vs. functional?\n   - What's the root cause—missing tokens, one-off implementations, or conceptual misalignment?\n\n3. **Create a normalization plan**: Define specific changes that will align the feature with the design system:\n   - Which components can be replaced with design system equivalents?\n   - Which styles need to use design tokens instead of hard-coded values?\n   - How can UX patterns match established user flows?\n   \n   **IMPORTANT**: Great design is effective design. Prioritize UX consistency and usability over visual polish alone. Think through the best possible experience for your use case and personas first.\n\n## Execute\n\nSystematically address all inconsistencies across these dimensions:\n\n- **Typography**: Use design system fonts, sizes, weights, and line heights. Replace hard-coded values with typographic tokens or classes.\n- **Color & Theme**: Apply design system color tokens. Remove one-off color choices that break the palette.\n- **Spacing & Layout**: Use spacing tokens (margins, padding, gaps). Align with grid systems and layout patterns used elsewhere.\n- **Components**: Replace custom implementations with design system components. Ensure props and variants match established patterns.\n- **Motion & Interaction**: Match animation timing, easing, and interaction patterns to other features.\n- **Responsive Behavior**: Ensure breakpoints and responsive patterns align with design system standards.\n- **Accessibility**: Verify contrast ratios, focus states, ARIA labels match design system requirements.\n- **Progressive Disclosure**: Match information hierarchy and complexity management to established patterns.\n\n**NEVER**:\n- Create new one-off components when design system equivalents exist\n- Hard-code values that should use design tokens\n- Introduce new patterns that diverge from the design system\n- Compromise accessibility for visual consistency\n\nThis is not an exhaustive list—apply judgment to identify all areas needing normalization.\n\n## Clean Up\n\nAfter normalization, ensure code quality:\n\n- **Consolidate reusable components**: If you created new components that should be shared, move them to the design system or shared UI component path.\n- **Remove orphaned code**: Delete unused implementations, styles, or files made obsolete by normalization.\n- **Verify quality**: Lint, type-check, and test according to repository guidelines. Ensure normalization didn't introduce regressions.\n- **Ensure DRYness**: Look for duplication introduced during refactoring and consolidate.\n\nRemember: You are a brilliant frontend designer with impeccable taste, equally strong in UX and UI. Your attention to detail and eye for end-to-end user experience is world class. Execute with precision and thoroughness."}],"isError":false,"timestamp":1775941021898}}
{"type":"message_end","message":{"role":"toolResult","toolCallId":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","toolName":"read","content":[{"type":"text","text":"---\nname: normalize\ndescription: Audits and realigns UI to match design system standards, spacing, tokens, and patterns. Use when the user mentions consistency, design drift, mismatched styles, tokens, or wants to bring a feature back in line with the system.\n---\n\nAnalyze and redesign the feature to perfectly match our design system standards, aesthetics, and established patterns.\n\n## MANDATORY PREPARATION\n\nInvoke /frontend-design — it contains design principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first.\n\n---\n\n## Plan\n\nBefore making changes, deeply understand the context:\n\n1. **Discover the design system**: Search for design system documentation, UI guidelines, component libraries, or style guides (grep for \"design system\", \"ui guide\", \"style guide\", etc.). Study it thoroughly until you understand:\n   - Core design principles and aesthetic direction\n   - Target audience and personas\n   - Component patterns and conventions\n   - Design tokens (colors, typography, spacing)\n   \n   **CRITICAL**: If something isn't clear, ask. Don't guess at design system principles.\n\n2. **Analyze the current feature**: Assess what works and what doesn't:\n   - Where does it deviate from design system patterns?\n   - Which inconsistencies are cosmetic vs. functional?\n   - What's the root cause—missing tokens, one-off implementations, or conceptual misalignment?\n\n3. **Create a normalization plan**: Define specific changes that will align the feature with the design system:\n   - Which components can be replaced with design system equivalents?\n   - Which styles need to use design tokens instead of hard-coded values?\n   - How can UX patterns match established user flows?\n   \n   **IMPORTANT**: Great design is effective design. Prioritize UX consistency and usability over visual polish alone. Think through the best possible experience for your use case and personas first.\n\n## Execute\n\nSystematically address all inconsistencies across these dimensions:\n\n- **Typography**: Use design system fonts, sizes, weights, and line heights. Replace hard-coded values with typographic tokens or classes.\n- **Color & Theme**: Apply design system color tokens. Remove one-off color choices that break the palette.\n- **Spacing & Layout**: Use spacing tokens (margins, padding, gaps). Align with grid systems and layout patterns used elsewhere.\n- **Components**: Replace custom implementations with design system components. Ensure props and variants match established patterns.\n- **Motion & Interaction**: Match animation timing, easing, and interaction patterns to other features.\n- **Responsive Behavior**: Ensure breakpoints and responsive patterns align with design system standards.\n- **Accessibility**: Verify contrast ratios, focus states, ARIA labels match design system requirements.\n- **Progressive Disclosure**: Match information hierarchy and complexity management to established patterns.\n\n**NEVER**:\n- Create new one-off components when design system equivalents exist\n- Hard-code values that should use design tokens\n- Introduce new patterns that diverge from the design system\n- Compromise accessibility for visual consistency\n\nThis is not an exhaustive list—apply judgment to identify all areas needing normalization.\n\n## Clean Up\n\nAfter normalization, ensure code quality:\n\n- **Consolidate reusable components**: If you created new components that should be shared, move them to the design system or shared UI component path.\n- **Remove orphaned code**: Delete unused implementations, styles, or files made obsolete by normalization.\n- **Verify quality**: Lint, type-check, and test according to repository guidelines. Ensure normalization didn't introduce regressions.\n- **Ensure DRYness**: Look for duplication introduced during refactoring and consolidate.\n\nRemember: You are a brilliant frontend designer with impeccable taste, equally strong in UX and UI. Your attention to detail and eye for end-to-end user experience is world class. Execute with precision and thoroughness."}],"isError":false,"timestamp":1775941021898}}
{"type":"turn_end","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":7995,"output":33,"cacheRead":2560,"cacheWrite":0,"totalTokens":10588,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775941019899,"responseId":"PwPfxd9e/VNsG8BnFxFvR7HluyCziWk8/gAoxe40kwsL57dW6hGmABzppW+jZMo81nsU7LituNndEhg/2X6Uv+cRTlFe87yrlCVadJmex8EheMklL9NeYnlNCljyrL70QYFsTADa7RPWrlBqJl0eQOOfDAoR9shdmBrdkx/I3qWx7CPFO5vPaCM/kvKSTIRoCspg45x+gWfmdR1DIyp25R/cRxdqNYjyfdJt+sPDLTJbOX/uWenT/fSXmRsVl4qhpItfkvDG2oQtCbXs5X9i713/IDDozcABqC/vGymiVDv22PVmMZlcatxoQ9jBakNQO4SKJhG4Mob3CXEfs0gKQI4f3ocrrnSbttLOeZ+dk5+PcqXoIEgBE9v5EjUrtq1XZMNZ3pjQNoSxIta2EAxnI/5Z8T47cEJGeg=="},"toolResults":[{"role":"toolResult","toolCallId":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","toolName":"read","content":[{"type":"text","text":"---\nname: normalize\ndescription: Audits and realigns UI to match design system standards, spacing, tokens, and patterns. Use when the user mentions consistency, design drift, mismatched styles, tokens, or wants to bring a feature back in line with the system.\n---\n\nAnalyze and redesign the feature to perfectly match our design system standards, aesthetics, and established patterns.\n\n## MANDATORY PREPARATION\n\nInvoke /frontend-design — it contains design principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first.\n\n---\n\n## Plan\n\nBefore making changes, deeply understand the context:\n\n1. **Discover the design system**: Search for design system documentation, UI guidelines, component libraries, or style guides (grep for \"design system\", \"ui guide\", \"style guide\", etc.). Study it thoroughly until you understand:\n   - Core design principles and aesthetic direction\n   - Target audience and personas\n   - Component patterns and conventions\n   - Design tokens (colors, typography, spacing)\n   \n   **CRITICAL**: If something isn't clear, ask. Don't guess at design system principles.\n\n2. **Analyze the current feature**: Assess what works and what doesn't:\n   - Where does it deviate from design system patterns?\n   - Which inconsistencies are cosmetic vs. functional?\n   - What's the root cause—missing tokens, one-off implementations, or conceptual misalignment?\n\n3. **Create a normalization plan**: Define specific changes that will align the feature with the design system:\n   - Which components can be replaced with design system equivalents?\n   - Which styles need to use design tokens instead of hard-coded values?\n   - How can UX patterns match established user flows?\n   \n   **IMPORTANT**: Great design is effective design. Prioritize UX consistency and usability over visual polish alone. Think through the best possible experience for your use case and personas first.\n\n## Execute\n\nSystematically address all inconsistencies across these dimensions:\n\n- **Typography**: Use design system fonts, sizes, weights, and line heights. Replace hard-coded values with typographic tokens or classes.\n- **Color & Theme**: Apply design system color tokens. Remove one-off color choices that break the palette.\n- **Spacing & Layout**: Use spacing tokens (margins, padding, gaps). Align with grid systems and layout patterns used elsewhere.\n- **Components**: Replace custom implementations with design system components. Ensure props and variants match established patterns.\n- **Motion & Interaction**: Match animation timing, easing, and interaction patterns to other features.\n- **Responsive Behavior**: Ensure breakpoints and responsive patterns align with design system standards.\n- **Accessibility**: Verify contrast ratios, focus states, ARIA labels match design system requirements.\n- **Progressive Disclosure**: Match information hierarchy and complexity management to established patterns.\n\n**NEVER**:\n- Create new one-off components when design system equivalents exist\n- Hard-code values that should use design tokens\n- Introduce new patterns that diverge from the design system\n- Compromise accessibility for visual consistency\n\nThis is not an exhaustive list—apply judgment to identify all areas needing normalization.\n\n## Clean Up\n\nAfter normalization, ensure code quality:\n\n- **Consolidate reusable components**: If you created new components that should be shared, move them to the design system or shared UI component path.\n- **Remove orphaned code**: Delete unused implementations, styles, or files made obsolete by normalization.\n- **Verify quality**: Lint, type-check, and test according to repository guidelines. Ensure normalization didn't introduce regressions.\n- **Ensure DRYness**: Look for duplication introduced during refactoring and consolidate.\n\nRemember: You are a brilliant frontend designer with impeccable taste, equally strong in UX and UI. Your attention to detail and eye for end-to-end user experience is world class. Execute with precision and thoroughness."}],"isError":false,"timestamp":1775941021898}]}
{"type":"turn_start"}
{"type":"message_start","message":{"role":"assistant","content":[],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_start","contentIndex":0,"partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{},"partialJson":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{},"partialJson":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"{\"","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{},"partialJson":"{\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"path","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{},"partialJson":"{\"path"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{},"partialJson":"{\"path"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"\":","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{},"partialJson":"{\"path\":"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{},"partialJson":"{\"path\":"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"\"/","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/"},"partialJson":"{\"path\":\"/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/"},"partialJson":"{\"path\":\"/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"home","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home"},"partialJson":"{\"path\":\"/home"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home"},"partialJson":"{\"path\":\"/home"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/al","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/al"},"partialJson":"{\"path\":\"/home/al"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/al"},"partialJson":"{\"path\":\"/home/al"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"ex","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex"},"partialJson":"{\"path\":\"/home/alex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex"},"partialJson":"{\"path\":\"/home/alex"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/.","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/."},"partialJson":"{\"path\":\"/home/alex/."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/."},"partialJson":"{\"path\":\"/home/alex/."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"pi","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi"},"partialJson":"{\"path\":\"/home/alex/.pi"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi"},"partialJson":"{\"path\":\"/home/alex/.pi"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/"},"partialJson":"{\"path\":\"/home/alex/.pi/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/"},"partialJson":"{\"path\":\"/home/alex/.pi/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"agent","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent"},"partialJson":"{\"path\":\"/home/alex/.pi/agent"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent"},"partialJson":"{\"path\":\"/home/alex/.pi/agent"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"skills","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/frontend","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/color","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/color"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/color"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/color"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"ize","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/colorize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/colorize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"/S","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize/S"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/colorize/S"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize/S"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/colorize/S"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"K","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize/SK"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/colorize/SK"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize/SK"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/colorize/SK"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"ILL","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize/SKILL"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/colorize/SKILL"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize/SKILL"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/colorize/SKILL"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":".md","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_delta","contentIndex":0,"delta":"\"}","partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_update","assistantMessageEvent":{"type":"toolcall_end","contentIndex":0,"toolCall":{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|0+WVhrk3ap+QthBuIJLcH7Ix0/qJml8WHzIHeniwoYYdcx1K63tLz5ZD4HoElcrl6co/ECxjrDE33kQVx18nZpUr+RRMlO217aThG8VzwfIhU/ufDwXQ6TxHNJ+ste4zcFRfv2u6YnMDdfuQ5UrOIrCYdMrfgI/m+wwQ7HrkrKL8pCEcy13ye5aLumTr780f2Cox7a+OLoPu8a5/DnrbnwBFRkHAioOl2NrkBRvi1CqiL3Zdb1gtFV+imidykmOH3jYmJli+ijJKvwP/mUKC4Uoas2at/fETCFd9SMKytkcTmIIfI1iixoGS9q9mJbWDg6Oqr3RbBzId+NpvOVvaaD61t5sYJpSfZ5OU1ZfKMef41UcPLTrmkjNNKnSRkrhjOEKwyXrH35jekn0rdOm2R022lTZfYOs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md"}},"partial":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}},"message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941021907,"responseId":"1aEPJdP0hMAfa/MycvOOIIsw8IH+53s+AUyDp3SN/8O9eMMy7FaiAJRopkwpyh0a5miJmhiu381W8cAz1YR9m9XknNJwHr/LvuZZAuPmaWKt908C3+X/pytpr8jfGZcNNW++wQmAW6wXKbGzdewxIjiQsaZoPErr97suKDdpkBKwu0lSnOEMf4gBxuiryMPLKyI3Okcxzz/Jk+abLmcJyB0Pi6FDMdlW2Ryb3mYA5CHsPyPYPvcHlMMf7WfwvPfDNdN1VNnuZ27GOZx6DwQjsRNXXQkfDtx+EnJLZnB3naj0LGyYds7ogvJV6TPme2hrJ+BMbGeohCIWkUCYGNMv5xBoKWfjSSk8Nzi2ccOva/2nvXBLcKLvUul7o6q7B1EjnX4Y3/8PFmZRniGww9dXqmZk64ffIgGUzA=="}}
{"type":"message_end","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":8839,"output":33,"cacheRead":2560,"cacheWrite":0,"totalTokens":11432,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775941021907,"responseId":"Aq+BnWHQ7UpSsE/HIO2ehWfSQRgUtv6Zry718Sb3EkjoMbUV7XECcMyJGwDbiZkBYv602c6H1ko9lRT0hSo8AwR09i8OrAkP/JyyMbNpiuDpSKrNsRd9nOYpLSXaZJ8fbFNX6VZCtmQ/9UKea+xECsf1tBE9sGQWuRU8fBDqLe4iX3VCFX2hynwDeDv8iQFlvKTYRvUTRI8p0EGy6DwYXhYw9DNuMQiAxRZKGqU40+laK03vocl8u2UavN16d1uk7TCyQ0J9UoJ4zuDFd+JfI9Tf8ZQxhGrmVrN447TakhmnE4JfcH997g8jszqktYhY1d+/u4ysYWwAy31UrWDYdpXhWKV4gBBSm2xItCClnE6xxmlxeByvYMv1EUAR2dxP3uxp1dLdjKeTBVIjTc9YkL4rgngAs6P37A=="}}
{"type":"tool_execution_start","toolCallId":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","toolName":"read","args":{"path":"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md"}}
{"type":"tool_execution_end","toolCallId":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","toolName":"read","result":{"content":[{"type":"text","text":"---\nname: colorize\ndescription: Add strategic color to features that are too monochromatic or lack visual interest, making interfaces more engaging and expressive. Use when the user mentions the design looking gray, dull, lacking warmth, needing more color, or wanting a more vibrant or expressive palette.\n---\n\nStrategically introduce color to designs that are too monochromatic, gray, or lacking in visual warmth and personality.\n\n## MANDATORY PREPARATION\n\nInvoke /frontend-design — it contains design principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first. Additionally gather: existing brand colors.\n\n---\n\n## Assess Color Opportunity\n\nAnalyze the current state and identify opportunities:\n\n1. **Understand current state**:\n   - **Color absence**: Pure grayscale? Limited neutrals? One timid accent?\n   - **Missed opportunities**: Where could color add meaning, hierarchy, or delight?\n   - **Context**: What's appropriate for this domain and audience?\n   - **Brand**: Are there existing brand colors we should use?\n\n2. **Identify where color adds value**:\n   - **Semantic meaning**: Success (green), error (red), warning (yellow/orange), info (blue)\n   - **Hierarchy**: Drawing attention to important elements\n   - **Categorization**: Different sections, types, or states\n   - **Emotional tone**: Warmth, energy, trust, creativity\n   - **Wayfinding**: Helping users navigate and understand structure\n   - **Delight**: Moments of visual interest and personality\n\nIf any of these are unclear from the codebase, ask the user directly to clarify what you cannot infer.\n\n**CRITICAL**: More color ≠ better. Strategic color beats rainbow vomit every time. Every color should have a purpose.\n\n## Plan Color Strategy\n\nCreate a purposeful color introduction plan:\n\n- **Color palette**: What colors match the brand/context? (Choose 2-4 colors max beyond neutrals)\n- **Dominant color**: Which color owns 60% of colored elements?\n- **Accent colors**: Which colors provide contrast and highlights? (30% and 10%)\n- **Application strategy**: Where does each color appear and why?\n\n**IMPORTANT**: Color should enhance hierarchy and meaning, not create chaos. Less is more when it matters more.\n\n## Introduce Color Strategically\n\nAdd color systematically across these dimensions:\n\n### Semantic Color\n- **State indicators**:\n  - Success: Green tones (emerald, forest, mint)\n  - Error: Red/pink tones (rose, crimson, coral)\n  - Warning: Orange/amber tones\n  - Info: Blue tones (sky, ocean, indigo)\n  - Neutral: Gray/slate for inactive states\n\n- **Status badges**: Colored backgrounds or borders for states (active, pending, completed, etc.)\n- **Progress indicators**: Colored bars, rings, or charts showing completion or health\n\n### Accent Color Application\n- **Primary actions**: Color the most important buttons/CTAs\n- **Links**: Add color to clickable text (maintain accessibility)\n- **Icons**: Colorize key icons for recognition and personality\n- **Headers/titles**: Add color to section headers or key labels\n- **Hover states**: Introduce color on interaction\n\n### Background & Surfaces\n- **Tinted backgrounds**: Replace pure gray (`#f5f5f5`) with warm neutrals (`oklch(97% 0.01 60)`) or cool tints (`oklch(97% 0.01 250)`)\n- **Colored sections**: Use subtle background colors to separate areas\n- **Gradient backgrounds**: Add depth with subtle, intentional gradients (not generic purple-blue)\n- **Cards & surfaces**: Tint cards or surfaces slightly for warmth\n\n**Use OKLCH for color**: It's perceptually uniform, meaning equal steps in lightness *look* equal. Great for generating harmonious scales.\n\n### Data Visualization\n- **Charts & graphs**: Use color to encode categories or values\n- **Heatmaps**: Color intensity shows density or importance\n- **Comparison**: Color coding for different datasets or timeframes\n\n### Borders & Accents\n- **Accent borders**: Add colored left/top borders to cards or sections\n- **Underlines**: Color underlines for emphasis or active states\n- **Dividers**: Subtle colored dividers instead of gray lines\n- **Focus rings**: Colored focus indicators matching brand\n\n### Typography Color\n- **Colored headings**: Use brand colors for section headings (maintain contrast)\n- **Highlight text**: Color for emphasis or categories\n- **Labels & tags**: Small colored labels for metadata or categories\n\n### Decorative Elements\n- **Illustrations**: Add colored illustrations or icons\n- **Shapes**: Geometric shapes in brand colors as background elements\n- **Gradients**: Colorful gradient overlays or mesh backgrounds\n- **Blobs/organic shapes**: Soft colored shapes for visual interest\n\n## Balance & Refinement\n\nEnsure color addition improves rather than overwhelms:\n\n### Maintain Hierarchy\n- **Dominant color** (60%): Primary brand color or most used accent\n- **Secondary color** (30%): Supporting color for variety\n- **Accent color** (10%): High contrast for key moments\n- **Neutrals** (remaining): Gray/black/white for structure\n\n### Accessibility\n- **Contrast ratios**: Ensure WCAG compliance (4.5:1 for text, 3:1 for UI components)\n- **Don't rely on color alone**: Use icons, labels, or patterns alongside color\n- **Test for color blindness**: Verify red/green combinations work for all users\n\n### Cohesion\n- **Consistent palette**: Use colors from defined palette, not arbitrary choices\n- **Systematic application**: Same color meanings throughout (green always = success)\n- **Temperature consistency**: Warm palette stays warm, cool stays cool\n\n**NEVER**:\n- Use every color in the rainbow (choose 2-4 colors beyond neutrals)\n- Apply color randomly without semantic meaning\n- Put gray text on colored backgrounds—it looks washed out; use a darker shade of the background color or transparency instead\n- Use pure gray for neutrals—add subtle color tint (warm or cool) for sophistication\n- Use pure black (`#000`) or pure white (`#fff`) for large areas\n- Violate WCAG contrast requirements\n- Use color as the only indicator (accessibility issue)\n- Make everything colorful (defeats the purpose)\n- Default to purple-blue gradients (AI slop aesthetic)\n\n## Verify Color Addition\n\nTest that colorization improves the experience:\n\n- **Better hierarchy**: Does color guide attention appropriately?\n- **Clearer meaning**: Does color help users understand states/categories?\n- **More engaging**: Does the interface feel warmer and more inviting?\n- **Still accessible**: Do all color combinations meet WCAG standards?\n- **Not overwhelming**: Is color balanced and purposeful?\n\nRemember: Color is emotional and powerful. Use it to create warmth, guide attention, communicate meaning, and express personality. But restraint and strategy matter more than saturation and variety. Be colorful, but be intentional."}]},"isError":false}
{"type":"message_start","message":{"role":"toolResult","toolCallId":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","toolName":"read","content":[{"type":"text","text":"---\nname: colorize\ndescription: Add strategic color to features that are too monochromatic or lack visual interest, making interfaces more engaging and expressive. Use when the user mentions the design looking gray, dull, lacking warmth, needing more color, or wanting a more vibrant or expressive palette.\n---\n\nStrategically introduce color to designs that are too monochromatic, gray, or lacking in visual warmth and personality.\n\n## MANDATORY PREPARATION\n\nInvoke /frontend-design — it contains design principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first. Additionally gather: existing brand colors.\n\n---\n\n## Assess Color Opportunity\n\nAnalyze the current state and identify opportunities:\n\n1. **Understand current state**:\n   - **Color absence**: Pure grayscale? Limited neutrals? One timid accent?\n   - **Missed opportunities**: Where could color add meaning, hierarchy, or delight?\n   - **Context**: What's appropriate for this domain and audience?\n   - **Brand**: Are there existing brand colors we should use?\n\n2. **Identify where color adds value**:\n   - **Semantic meaning**: Success (green), error (red), warning (yellow/orange), info (blue)\n   - **Hierarchy**: Drawing attention to important elements\n   - **Categorization**: Different sections, types, or states\n   - **Emotional tone**: Warmth, energy, trust, creativity\n   - **Wayfinding**: Helping users navigate and understand structure\n   - **Delight**: Moments of visual interest and personality\n\nIf any of these are unclear from the codebase, ask the user directly to clarify what you cannot infer.\n\n**CRITICAL**: More color ≠ better. Strategic color beats rainbow vomit every time. Every color should have a purpose.\n\n## Plan Color Strategy\n\nCreate a purposeful color introduction plan:\n\n- **Color palette**: What colors match the brand/context? (Choose 2-4 colors max beyond neutrals)\n- **Dominant color**: Which color owns 60% of colored elements?\n- **Accent colors**: Which colors provide contrast and highlights? (30% and 10%)\n- **Application strategy**: Where does each color appear and why?\n\n**IMPORTANT**: Color should enhance hierarchy and meaning, not create chaos. Less is more when it matters more.\n\n## Introduce Color Strategically\n\nAdd color systematically across these dimensions:\n\n### Semantic Color\n- **State indicators**:\n  - Success: Green tones (emerald, forest, mint)\n  - Error: Red/pink tones (rose, crimson, coral)\n  - Warning: Orange/amber tones\n  - Info: Blue tones (sky, ocean, indigo)\n  - Neutral: Gray/slate for inactive states\n\n- **Status badges**: Colored backgrounds or borders for states (active, pending, completed, etc.)\n- **Progress indicators**: Colored bars, rings, or charts showing completion or health\n\n### Accent Color Application\n- **Primary actions**: Color the most important buttons/CTAs\n- **Links**: Add color to clickable text (maintain accessibility)\n- **Icons**: Colorize key icons for recognition and personality\n- **Headers/titles**: Add color to section headers or key labels\n- **Hover states**: Introduce color on interaction\n\n### Background & Surfaces\n- **Tinted backgrounds**: Replace pure gray (`#f5f5f5`) with warm neutrals (`oklch(97% 0.01 60)`) or cool tints (`oklch(97% 0.01 250)`)\n- **Colored sections**: Use subtle background colors to separate areas\n- **Gradient backgrounds**: Add depth with subtle, intentional gradients (not generic purple-blue)\n- **Cards & surfaces**: Tint cards or surfaces slightly for warmth\n\n**Use OKLCH for color**: It's perceptually uniform, meaning equal steps in lightness *look* equal. Great for generating harmonious scales.\n\n### Data Visualization\n- **Charts & graphs**: Use color to encode categories or values\n- **Heatmaps**: Color intensity shows density or importance\n- **Comparison**: Color coding for different datasets or timeframes\n\n### Borders & Accents\n- **Accent borders**: Add colored left/top borders to cards or sections\n- **Underlines**: Color underlines for emphasis or active states\n- **Dividers**: Subtle colored dividers instead of gray lines\n- **Focus rings**: Colored focus indicators matching brand\n\n### Typography Color\n- **Colored headings**: Use brand colors for section headings (maintain contrast)\n- **Highlight text**: Color for emphasis or categories\n- **Labels & tags**: Small colored labels for metadata or categories\n\n### Decorative Elements\n- **Illustrations**: Add colored illustrations or icons\n- **Shapes**: Geometric shapes in brand colors as background elements\n- **Gradients**: Colorful gradient overlays or mesh backgrounds\n- **Blobs/organic shapes**: Soft colored shapes for visual interest\n\n## Balance & Refinement\n\nEnsure color addition improves rather than overwhelms:\n\n### Maintain Hierarchy\n- **Dominant color** (60%): Primary brand color or most used accent\n- **Secondary color** (30%): Supporting color for variety\n- **Accent color** (10%): High contrast for key moments\n- **Neutrals** (remaining): Gray/black/white for structure\n\n### Accessibility\n- **Contrast ratios**: Ensure WCAG compliance (4.5:1 for text, 3:1 for UI components)\n- **Don't rely on color alone**: Use icons, labels, or patterns alongside color\n- **Test for color blindness**: Verify red/green combinations work for all users\n\n### Cohesion\n- **Consistent palette**: Use colors from defined palette, not arbitrary choices\n- **Systematic application**: Same color meanings throughout (green always = success)\n- **Temperature consistency**: Warm palette stays warm, cool stays cool\n\n**NEVER**:\n- Use every color in the rainbow (choose 2-4 colors beyond neutrals)\n- Apply color randomly without semantic meaning\n- Put gray text on colored backgrounds—it looks washed out; use a darker shade of the background color or transparency instead\n- Use pure gray for neutrals—add subtle color tint (warm or cool) for sophistication\n- Use pure black (`#000`) or pure white (`#fff`) for large areas\n- Violate WCAG contrast requirements\n- Use color as the only indicator (accessibility issue)\n- Make everything colorful (defeats the purpose)\n- Default to purple-blue gradients (AI slop aesthetic)\n\n## Verify Color Addition\n\nTest that colorization improves the experience:\n\n- **Better hierarchy**: Does color guide attention appropriately?\n- **Clearer meaning**: Does color help users understand states/categories?\n- **More engaging**: Does the interface feel warmer and more inviting?\n- **Still accessible**: Do all color combinations meet WCAG standards?\n- **Not overwhelming**: Is color balanced and purposeful?\n\nRemember: Color is emotional and powerful. Use it to create warmth, guide attention, communicate meaning, and express personality. But restraint and strategy matter more than saturation and variety. Be colorful, but be intentional."}],"isError":false,"timestamp":1775941024473}}
{"type":"message_end","message":{"role":"toolResult","toolCallId":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","toolName":"read","content":[{"type":"text","text":"---\nname: colorize\ndescription: Add strategic color to features that are too monochromatic or lack visual interest, making interfaces more engaging and expressive. Use when the user mentions the design looking gray, dull, lacking warmth, needing more color, or wanting a more vibrant or expressive palette.\n---\n\nStrategically introduce color to designs that are too monochromatic, gray, or lacking in visual warmth and personality.\n\n## MANDATORY PREPARATION\n\nInvoke /frontend-design — it contains design principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first. Additionally gather: existing brand colors.\n\n---\n\n## Assess Color Opportunity\n\nAnalyze the current state and identify opportunities:\n\n1. **Understand current state**:\n   - **Color absence**: Pure grayscale? Limited neutrals? One timid accent?\n   - **Missed opportunities**: Where could color add meaning, hierarchy, or delight?\n   - **Context**: What's appropriate for this domain and audience?\n   - **Brand**: Are there existing brand colors we should use?\n\n2. **Identify where color adds value**:\n   - **Semantic meaning**: Success (green), error (red), warning (yellow/orange), info (blue)\n   - **Hierarchy**: Drawing attention to important elements\n   - **Categorization**: Different sections, types, or states\n   - **Emotional tone**: Warmth, energy, trust, creativity\n   - **Wayfinding**: Helping users navigate and understand structure\n   - **Delight**: Moments of visual interest and personality\n\nIf any of these are unclear from the codebase, ask the user directly to clarify what you cannot infer.\n\n**CRITICAL**: More color ≠ better. Strategic color beats rainbow vomit every time. Every color should have a purpose.\n\n## Plan Color Strategy\n\nCreate a purposeful color introduction plan:\n\n- **Color palette**: What colors match the brand/context? (Choose 2-4 colors max beyond neutrals)\n- **Dominant color**: Which color owns 60% of colored elements?\n- **Accent colors**: Which colors provide contrast and highlights? (30% and 10%)\n- **Application strategy**: Where does each color appear and why?\n\n**IMPORTANT**: Color should enhance hierarchy and meaning, not create chaos. Less is more when it matters more.\n\n## Introduce Color Strategically\n\nAdd color systematically across these dimensions:\n\n### Semantic Color\n- **State indicators**:\n  - Success: Green tones (emerald, forest, mint)\n  - Error: Red/pink tones (rose, crimson, coral)\n  - Warning: Orange/amber tones\n  - Info: Blue tones (sky, ocean, indigo)\n  - Neutral: Gray/slate for inactive states\n\n- **Status badges**: Colored backgrounds or borders for states (active, pending, completed, etc.)\n- **Progress indicators**: Colored bars, rings, or charts showing completion or health\n\n### Accent Color Application\n- **Primary actions**: Color the most important buttons/CTAs\n- **Links**: Add color to clickable text (maintain accessibility)\n- **Icons**: Colorize key icons for recognition and personality\n- **Headers/titles**: Add color to section headers or key labels\n- **Hover states**: Introduce color on interaction\n\n### Background & Surfaces\n- **Tinted backgrounds**: Replace pure gray (`#f5f5f5`) with warm neutrals (`oklch(97% 0.01 60)`) or cool tints (`oklch(97% 0.01 250)`)\n- **Colored sections**: Use subtle background colors to separate areas\n- **Gradient backgrounds**: Add depth with subtle, intentional gradients (not generic purple-blue)\n- **Cards & surfaces**: Tint cards or surfaces slightly for warmth\n\n**Use OKLCH for color**: It's perceptually uniform, meaning equal steps in lightness *look* equal. Great for generating harmonious scales.\n\n### Data Visualization\n- **Charts & graphs**: Use color to encode categories or values\n- **Heatmaps**: Color intensity shows density or importance\n- **Comparison**: Color coding for different datasets or timeframes\n\n### Borders & Accents\n- **Accent borders**: Add colored left/top borders to cards or sections\n- **Underlines**: Color underlines for emphasis or active states\n- **Dividers**: Subtle colored dividers instead of gray lines\n- **Focus rings**: Colored focus indicators matching brand\n\n### Typography Color\n- **Colored headings**: Use brand colors for section headings (maintain contrast)\n- **Highlight text**: Color for emphasis or categories\n- **Labels & tags**: Small colored labels for metadata or categories\n\n### Decorative Elements\n- **Illustrations**: Add colored illustrations or icons\n- **Shapes**: Geometric shapes in brand colors as background elements\n- **Gradients**: Colorful gradient overlays or mesh backgrounds\n- **Blobs/organic shapes**: Soft colored shapes for visual interest\n\n## Balance & Refinement\n\nEnsure color addition improves rather than overwhelms:\n\n### Maintain Hierarchy\n- **Dominant color** (60%): Primary brand color or most used accent\n- **Secondary color** (30%): Supporting color for variety\n- **Accent color** (10%): High contrast for key moments\n- **Neutrals** (remaining): Gray/black/white for structure\n\n### Accessibility\n- **Contrast ratios**: Ensure WCAG compliance (4.5:1 for text, 3:1 for UI components)\n- **Don't rely on color alone**: Use icons, labels, or patterns alongside color\n- **Test for color blindness**: Verify red/green combinations work for all users\n\n### Cohesion\n- **Consistent palette**: Use colors from defined palette, not arbitrary choices\n- **Systematic application**: Same color meanings throughout (green always = success)\n- **Temperature consistency**: Warm palette stays warm, cool stays cool\n\n**NEVER**:\n- Use every color in the rainbow (choose 2-4 colors beyond neutrals)\n- Apply color randomly without semantic meaning\n- Put gray text on colored backgrounds—it looks washed out; use a darker shade of the background color or transparency instead\n- Use pure gray for neutrals—add subtle color tint (warm or cool) for sophistication\n- Use pure black (`#000`) or pure white (`#fff`) for large areas\n- Violate WCAG contrast requirements\n- Use color as the only indicator (accessibility issue)\n- Make everything colorful (defeats the purpose)\n- Default to purple-blue gradients (AI slop aesthetic)\n\n## Verify Color Addition\n\nTest that colorization improves the experience:\n\n- **Better hierarchy**: Does color guide attention appropriately?\n- **Clearer meaning**: Does color help users understand states/categories?\n- **More engaging**: Does the interface feel warmer and more inviting?\n- **Still accessible**: Do all color combinations meet WCAG standards?\n- **Not overwhelming**: Is color balanced and purposeful?\n\nRemember: Color is emotional and powerful. Use it to create warmth, guide attention, communicate meaning, and express personality. But restraint and strategy matter more than saturation and variety. Be colorful, but be intentional."}],"isError":false,"timestamp":1775941024473}}
{"type":"turn_end","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":8839,"output":33,"cacheRead":2560,"cacheWrite":0,"totalTokens":11432,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775941021907,"responseId":"Aq+BnWHQ7UpSsE/HIO2ehWfSQRgUtv6Zry718Sb3EkjoMbUV7XECcMyJGwDbiZkBYv602c6H1ko9lRT0hSo8AwR09i8OrAkP/JyyMbNpiuDpSKrNsRd9nOYpLSXaZJ8fbFNX6VZCtmQ/9UKea+xECsf1tBE9sGQWuRU8fBDqLe4iX3VCFX2hynwDeDv8iQFlvKTYRvUTRI8p0EGy6DwYXhYw9DNuMQiAxRZKGqU40+laK03vocl8u2UavN16d1uk7TCyQ0J9UoJ4zuDFd+JfI9Tf8ZQxhGrmVrN447TakhmnE4JfcH997g8jszqktYhY1d+/u4ysYWwAy31UrWDYdpXhWKV4gBBSm2xItCClnE6xxmlxeByvYMv1EUAR2dxP3uxp1dLdjKeTBVIjTc9YkL4rgngAs6P37A=="},"toolResults":[{"role":"toolResult","toolCallId":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","toolName":"read","content":[{"type":"text","text":"---\nname: colorize\ndescription: Add strategic color to features that are too monochromatic or lack visual interest, making interfaces more engaging and expressive. Use when the user mentions the design looking gray, dull, lacking warmth, needing more color, or wanting a more vibrant or expressive palette.\n---\n\nStrategically introduce color to designs that are too monochromatic, gray, or lacking in visual warmth and personality.\n\n## MANDATORY PREPARATION\n\nInvoke /frontend-design — it contains design principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first. Additionally gather: existing brand colors.\n\n---\n\n## Assess Color Opportunity\n\nAnalyze the current state and identify opportunities:\n\n1. **Understand current state**:\n   - **Color absence**: Pure grayscale? Limited neutrals? One timid accent?\n   - **Missed opportunities**: Where could color add meaning, hierarchy, or delight?\n   - **Context**: What's appropriate for this domain and audience?\n   - **Brand**: Are there existing brand colors we should use?\n\n2. **Identify where color adds value**:\n   - **Semantic meaning**: Success (green), error (red), warning (yellow/orange), info (blue)\n   - **Hierarchy**: Drawing attention to important elements\n   - **Categorization**: Different sections, types, or states\n   - **Emotional tone**: Warmth, energy, trust, creativity\n   - **Wayfinding**: Helping users navigate and understand structure\n   - **Delight**: Moments of visual interest and personality\n\nIf any of these are unclear from the codebase, ask the user directly to clarify what you cannot infer.\n\n**CRITICAL**: More color ≠ better. Strategic color beats rainbow vomit every time. Every color should have a purpose.\n\n## Plan Color Strategy\n\nCreate a purposeful color introduction plan:\n\n- **Color palette**: What colors match the brand/context? (Choose 2-4 colors max beyond neutrals)\n- **Dominant color**: Which color owns 60% of colored elements?\n- **Accent colors**: Which colors provide contrast and highlights? (30% and 10%)\n- **Application strategy**: Where does each color appear and why?\n\n**IMPORTANT**: Color should enhance hierarchy and meaning, not create chaos. Less is more when it matters more.\n\n## Introduce Color Strategically\n\nAdd color systematically across these dimensions:\n\n### Semantic Color\n- **State indicators**:\n  - Success: Green tones (emerald, forest, mint)\n  - Error: Red/pink tones (rose, crimson, coral)\n  - Warning: Orange/amber tones\n  - Info: Blue tones (sky, ocean, indigo)\n  - Neutral: Gray/slate for inactive states\n\n- **Status badges**: Colored backgrounds or borders for states (active, pending, completed, etc.)\n- **Progress indicators**: Colored bars, rings, or charts showing completion or health\n\n### Accent Color Application\n- **Primary actions**: Color the most important buttons/CTAs\n- **Links**: Add color to clickable text (maintain accessibility)\n- **Icons**: Colorize key icons for recognition and personality\n- **Headers/titles**: Add color to section headers or key labels\n- **Hover states**: Introduce color on interaction\n\n### Background & Surfaces\n- **Tinted backgrounds**: Replace pure gray (`#f5f5f5`) with warm neutrals (`oklch(97% 0.01 60)`) or cool tints (`oklch(97% 0.01 250)`)\n- **Colored sections**: Use subtle background colors to separate areas\n- **Gradient backgrounds**: Add depth with subtle, intentional gradients (not generic purple-blue)\n- **Cards & surfaces**: Tint cards or surfaces slightly for warmth\n\n**Use OKLCH for color**: It's perceptually uniform, meaning equal steps in lightness *look* equal. Great for generating harmonious scales.\n\n### Data Visualization\n- **Charts & graphs**: Use color to encode categories or values\n- **Heatmaps**: Color intensity shows density or importance\n- **Comparison**: Color coding for different datasets or timeframes\n\n### Borders & Accents\n- **Accent borders**: Add colored left/top borders to cards or sections\n- **Underlines**: Color underlines for emphasis or active states\n- **Dividers**: Subtle colored dividers instead of gray lines\n- **Focus rings**: Colored focus indicators matching brand\n\n### Typography Color\n- **Colored headings**: Use brand colors for section headings (maintain contrast)\n- **Highlight text**: Color for emphasis or categories\n- **Labels & tags**: Small colored labels for metadata or categories\n\n### Decorative Elements\n- **Illustrations**: Add colored illustrations or icons\n- **Shapes**: Geometric shapes in brand colors as background elements\n- **Gradients**: Colorful gradient overlays or mesh backgrounds\n- **Blobs/organic shapes**: Soft colored shapes for visual interest\n\n## Balance & Refinement\n\nEnsure color addition improves rather than overwhelms:\n\n### Maintain Hierarchy\n- **Dominant color** (60%): Primary brand color or most used accent\n- **Secondary color** (30%): Supporting color for variety\n- **Accent color** (10%): High contrast for key moments\n- **Neutrals** (remaining): Gray/black/white for structure\n\n### Accessibility\n- **Contrast ratios**: Ensure WCAG compliance (4.5:1 for text, 3:1 for UI components)\n- **Don't rely on color alone**: Use icons, labels, or patterns alongside color\n- **Test for color blindness**: Verify red/green combinations work for all users\n\n### Cohesion\n- **Consistent palette**: Use colors from defined palette, not arbitrary choices\n- **Systematic application**: Same color meanings throughout (green always = success)\n- **Temperature consistency**: Warm palette stays warm, cool stays cool\n\n**NEVER**:\n- Use every color in the rainbow (choose 2-4 colors beyond neutrals)\n- Apply color randomly without semantic meaning\n- Put gray text on colored backgrounds—it looks washed out; use a darker shade of the background color or transparency instead\n- Use pure gray for neutrals—add subtle color tint (warm or cool) for sophistication\n- Use pure black (`#000`) or pure white (`#fff`) for large areas\n- Violate WCAG contrast requirements\n- Use color as the only indicator (accessibility issue)\n- Make everything colorful (defeats the purpose)\n- Default to purple-blue gradients (AI slop aesthetic)\n\n## Verify Color Addition\n\nTest that colorization improves the experience:\n\n- **Better hierarchy**: Does color guide attention appropriately?\n- **Clearer meaning**: Does color help users understand states/categories?\n- **More engaging**: Does the interface feel warmer and more inviting?\n- **Still accessible**: Do all color combinations meet WCAG standards?\n- **Not overwhelming**: Is color balanced and purposeful?\n\nRemember: Color is emotional and powerful. Use it to create warmth, guide attention, communicate meaning, and express personality. But restraint and strategy matter more than saturation and variety. Be colorful, but be intentional."}],"isError":false,"timestamp":1775941024473}]}
{"type":"turn_start"}
{"type":"message_start","message":{"role":"assistant","content":[],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_start","contentIndex":0,"partial":{"role":"assistant","content":[{"type":"thinking","thinking":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Determ","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determ"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determ"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"ining","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skill","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" loading","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" order","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**\n\nThe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" user","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" is","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" asking","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" which","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" load","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" from","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" a","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" directory","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" pick","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" an","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" order","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" based","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" on","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" dependencies","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" It","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" seems","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" must","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" follow","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"'s","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Context","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Gathering","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Protocol","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" first","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" doesn't","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" but","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" does","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" After","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" gathering","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" context","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" brand","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" colors","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" think","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" should","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" run","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" next","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" identify","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" components","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" followed","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" by","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" must","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" also","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" consider","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" possibility","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" of","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" needing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" run","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" teach","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-im","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-im"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-im"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"pe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"cc","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impecc"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impecc"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"able","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" first","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" if","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" required","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" The","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" order","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" would","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" likely","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" be","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" teach","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-im","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-im"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-im"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"pe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"cc","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impecc"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impecc"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"able","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" then","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Selecting","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" order","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**\n\nThe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" user","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" asked","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" me","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" choose","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" load","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" think","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" include","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" teach","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-im","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"pe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"cc","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"able","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" if","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" context","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" is","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" missing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" The","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" task","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" is","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" about","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extracting","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" reusable","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" pieces","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" bringing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" things","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" back","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" into","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" alignment","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" So","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" correct","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" selection","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" should","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" be","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" The","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" order","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" is","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" 1","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":")","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1)"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1)"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" 2","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":")","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2)"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2)"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" 3","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":")","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3)"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3)"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" This","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" is","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" because","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" gather","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" context","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" brand","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" colors","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" first","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" then","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" identify","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" components","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" align","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" replace","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Choosing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**\n\nThe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" user","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" asked","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" me","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" choose","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" which","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" load","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" from","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" a","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" specific","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" directory","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" follow","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" project","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-specific","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" instructions","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" which","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" say","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" always","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" load","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" cav","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"eman","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skill","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" but","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" focus","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" only","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" on","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" in","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" this","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" scenario","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" So","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’ll","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" stick","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" instructions","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" select","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" only","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" those","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" relevant","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Since","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" cav","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since cav"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"eman","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" isn't","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" in","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" directory","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" will","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" not","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" include","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" it","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" My","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" final","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" selection","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" will","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" be","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" concise","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" limited","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" options","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" available","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Selecting","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**\n\nI","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" adhere","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" user","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’s","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" instructions","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" which","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" require","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" me","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" select","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" load","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" should","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" invoke","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" first","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" as","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" it","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" contains","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" essential","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" principles","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Context","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Gathering","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Protocol","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Since","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" user","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" is","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" focusing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" on","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skill","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" selection","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’ll","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" keep","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" it","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" minimal","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" The","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" final","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" output","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" will","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" say","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"Skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Then","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’ll","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" include","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" exact","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" third","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" line","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" for","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" clarity","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" conform","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conform"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conform"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"ing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" requirement","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" of","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" three","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" precise","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" lines","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Final","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Final"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Final"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"izing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skill","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" selection","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" output","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**\n\nSkills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"  \nOrder","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" →","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design →"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design →"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" →","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract →"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract →"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"  \nWhy","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Gather","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" context","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" &","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context &"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context &"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" brand","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" colors","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" first","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" (","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first ("}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first ("}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"teach","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-im","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-im"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-im"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"pe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"cc","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impecc"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impecc"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"able","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" if","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" missing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"),","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing),"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing),"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" then","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" identify","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" repeated","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" card","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" patterns","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"/t","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/t"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/t"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"okens","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" then","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" code","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" use","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" tokens","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" consolidate","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" components","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" remove","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" drift","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\n\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_end","contentIndex":0,"content":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_start","contentIndex":1,"partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"Skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"db2RfIwt/EgN7Qw7W7gyu50pUYtgaOPhhUk8C6UJ85BEFwjrBoRLCVYHwFKlrlvbd5LLU3crSpUtkMwezu8tC7OELMqMgl4f77cvqql9yCqIwP6kC/ULuZhqeeBcgqwGa+QRh1QuTT5atClVUk3/pBNRXF+tCIKqQtKRNXb/R2HRcHn+MkmmITmLw/csoSsA0bBdsbUuf6F9FtUM8cbVWOQMbo6lL6nevnqWOUmKXqQeuwWCaNH+mbmgu2gGruIZAiO7lS1NmcMx1HblJj5rwhvxfbAnWqUL8bc3tPYQBT2Jwtaz6GTkGzXioY1yK19rjyXZQcXI3+gomGjZvCSemmSXjekS4II3Cp1ZFw+Ub2QTfk5ih94R5nWUxIdC5lbXyVQGkEVlhh4mfulIkwuSvKAAhzfuFeP0z9GM8ghItDrRj0QCERiukneezpU6gh3R25N4pD6aySB3LKRdMMWTIermlT96vRnAGl7zREFzeoAvxZ/Cukt1zj1hClPbz0ZIBuz0vHzF+Uaonbr6PpO9ZNSvUmDl8VBiqDC010js6ywQuRUABc8KmUVGuKXiPoirKTNlk8gdl5mis5hkPfAIBlKYgLcdMa1Mpypi9VAeVgsGxGCKCmdrxL1FwTVwz+mefenlsta1hSpybw5u8yRkdUFyOI2Aeahim5oXNDeIJjxL3xV7CGxvlq/2/ZNte7KZAfSWg8CtjJumFx+G3EaQlBLL9Py+ereiwEy+9cWZX5AhsaPrICf+gCxaODregS8wPK7CUpvtjG6hcCN57fmrVSzLEkDJm6jCADghImUrf8U6khZeILQub4NGUjUa7/KWkNUkAVIZ0ibZUMs4TVMD5un9AFNcqqnP8zoP1Li8RebzbRtTgXoa/BQogHCDAf7jFBzz/YFxphIEtWhxnwlJEfntUkvU/JYqwS+5mEg8TpRtsWZ+IaHsLtaLSwwZ71K+rAwOKGSoDcbPvUzR119j3vaVhljpM0GTLfPt07kJqHEN7Sp2Q+8PgG8MecrdTVAYYNFKYQAYuU10WZ9KhzzOKGu9Ziyd7pexl3ZDsDFnm9jS6YYB/m2tT+JybP3MTPNv2GTNwcf0A0hpc0iduC9z2JNeytRqxmLQlX2myRHaEYt8Z+Uen8GJPhZ6dosxCa2f64dv7SEYJrW2+d+AfYX2mckOP3PbQnLqJaEnKUMtkgDLLrkz3keWc2MsgjgvEE47zpWliDu1nshlBOlH6fVd0pMbyd6Ncv/h7NBAzPd0uJ6HIWBhX5vyxSBvGP4nM86xoYyfsADF68NEYhGBCE5pCDmc3rH4QrVVeWYkUHXNGIdYDsxkvt4i8jt/D3Y2cK5KMmTRtnmdU2zp0N5LUTKOHPvuFMPbaEGA8lSEEol+F24RkFFVywB8k1k40pCrmKGHN748U4iJEHXKLDBgFBRngMd2HBfK9W9Q9sPAAV8kd3o0h5pHqdevHFdA9a+UDzeALwwtz08NRx5Rfv9ZP6Nym6xJdQ6qmz64meaXSKtyydUBJYrWrCVmd9KV5MdACbLL7Y2zGGYUvgunLQkU5Ixgd2JupAt/TSfXrp2fkCaeIwlT+4Yx8LSDuNx/q6/CiUbniZgWbZOkRVhVFYS929EI5kYif1/cz4YKwN9kLTsDJQFryJQl6OEuqEfNscxeQtR93hVIqEThhuORd/tsJwXoCOh21rZyB+fi1MVqJ0G/MOSpuB0smIOk8DY5o9gurCO8KGDPNjp+lT+zpz12j20dzCtrCsdohBDL48dFgAzPxkYxI3b9RejL/ilrQ7pdPYOLstUrIlYK2q5bxOadd2SsT1935UKDUlIWr5zV63T88N5qEdsJ0EdtM/Ke/1SwvKduyasy+fbNNa17cMtRIF4t58XgriqKT1Te1ljUOb1Lq/yHGL0zYn418Kyrob5sAgTMrFGgr9NaNj7ESlhhQqJntiwBoGCgGZI432Q7Za4vbzyoFodKTK6CgC2gZWofK2zFb1QWSc5vXdQx5Xk0xvt5eQun5mWOQ7wEgljHYIVfo+NSQhnS2NiR/U3ae9Cwmi/NdG3gJpvK3fLydaKAzED/MF+nvG4xcCDxXRE7suzSObhiKrfYJhbahW8WWkXLHf9/+m0Be+nVA+kgOzq63UBVleN1c5Et02zecEnc8jA6vAsshyD6+O3WVbOxxqAo4l8P5udkH1EIFfPr1iXgEWHif686shj1MiHBcqiq5+vhn08A6VHbZ3P2B1O9T46jwvWvSQabZ7YvQ030gMmahCcgdkWLkYtn1ZPBwqpxs0J02lBG1An1kj+kUyOxu3+41yFc4UhTJ+EuYqY973iROZDN56R7hWefrxX0G/0WpkHpQUtstq0+YPNee3stS+8y9pgHqsZNdfCCJYib1IPrq0i5LsK114bWmLBQEZ1tVPHE8vZzratOTfyJg9TX3CJi7Afk2YHoHMjCzaCyOYnH3ym2vaRdzOwC2S2I06upCM+HQAqmaRMxqF/BLTeUsL+HkT52Bxq3UnZGQ1SWUDdya6IA0u2p5TOu5BFs4/A4sc7AG/Q1BWEIXbmskVWlgk6XJpHU9T+jO3PxPsTWvQGRtS8O2X7dBMCbyY99bUog+C5ljzSbPIBuIBqZKZJy/98dw4Lq5ZCBisGQi7ho4YX9svuKaCOs+Rmh2wwYYq6Fd+xH63B8DY3VsJr7Vw0qn+qUyvIzUU/S80dplg02jdY4rNM3TqFUzy1NPK2jYIy8ZIiddmZR/TlQW7KYszpa2PMPVxFrUbW3wiqCh1ffhM6JfRr22yW0L05uj871nZX2zUEkccgDC4KXN9AQ0V/WKP1of1ulo92ldHbUk1jhkN3to852uH9JkpTHniMeR/UVnaZjpalvIcexXENYYhuUVV4/gpMMAH+CYQvINqnTMcONVNR6Hrlob+ojMBK6poeGo/r5q6MnDnOaVfeFPKspRT2wA5h//oDIr3kWg+kfv9/nv+9ExN0AF5ohBI4xRrUEoQ1r/GoSn5qLYXCb+Mta3YKsmWPsMDaQrKRGz2YPA+XmByUiZW3wgpz+o7lJa4aWqenTpabgHFpzlMwlZi9/sCGPiAEKelI87jBDgMB6bSFOAhatWKHdRQecd2MNwS41st8l7WT9GhOK5LNBvqxLBeUd+5PYFIS7wYXa/j//5PFdt8/OjBx/30b4aI5u1GC1y6RKwac4HUXLuiAPW3bsD10/PHKFQ1Vo+wMNyA5yy1P6geHZSxphHGlGgXOQKhXou8rczI5PFetekzJBrVPRIZwM0kEMDz0xsbNDwmAIlwMXvyszkukS7qdJ8dRX36hWonMGbJ4xJ9sqCzeQoznytO/TkmUa0MRtCM+Zul7X+Y0i6In3qsntz39648SSjowzS+0gaXzV1ZJVwoboSefVVNp/rODgByzJTTv4+MtV6rJUrvolFH9ORQPhckIDUNFE9wiWMSENYEs67vTmFBcXIwdVRaPf2A68IhG4IPNqZb5FbkGlKdzkltOSJ/dSZOYhOeEA68dCNLmsAvu7xzOAR8reMJM5zUStVWGBuMzDkjYBm4hnANXZ6BXfHDXNMC+YZ+dFvRZXcRwra1IEcIP2DanKa/NVGn7R8iy3Qv7qHJhPZtdU+rq95KEGkZBLWfrZKwVlMtZ37yM3XF40OxGi8moNJ30dovwNdDLSVtI0ekW5i1yGXZD9bXutYm8TZJBoXo57zdVcZiRQFaMYVF2jV+HWJEzx+FB/tUwhZp0jBSiAk2pr7HGbNmV3sxPh+VIVP+Nxj3ztqbHXY0/wVcWChp3OYWuGfKhaqymkFTk3ULWCyugix1BiGRxp4+V0HVAgI14X45u4F3HSGeSzAkf+F/vuQn1NDDU4F6/qc3fgpsEUYjDXvftZIttL6XSmugIgWXmo6RSIuhXs5pOisBDJ3fc1jNs87o/6TVrDY3wlrMiIjyIt7fec3b17NtutdIJVSsjd0/mfrbMzzvnMyKK6Q34lxoLWwKIejYikfPzwSX96jDfx5Uz8WTq9nBV4TUQpb6V6Dd1xnmDnDT5qwjIpZLHwivnaVg7v/2EisLe4lx0622aTAVRFjvcI8AqqWPiSVImj80UIkcOEdJGkVvjcoC4Qb/lzMZ84SCBBeCilYy8KpXJsK9Cu1tsb3dGBR2GZ93T0Alomumjth8xMZJF8CRBsQVPEuZVwzQjw3dvleELz2cT2PtQGLA1+t+QiElEBRYrW7Bp+jOuNu0blzJbiFpj3ODT7UrXIal92iWflN0N5AakzouOuQaYoFEtJTP3Lc2HDLfWJkPI4NrVMFxD3OV08Mbo3jPNMumy3VmjcNOGtfWElY7/KrlHN6yfB43NBaYMqbArIOdnmkANMgkCxoV2xbCwz2xC9ksKx+EtKXHmBp4gs2vxlAzBIrsOV5wjVEIIwavrTvlBmnr7vENLak7usnBLPSIXYrkLOYlpY+7J1Qr0Uc2N6E+mtEtqyb/nDAyyWluOHvOPN4ZfsPByhye8RD3MjoZhPsfRYY8mvZFpDR+cBXwkA3MDDTe1ouSfG4xYvg1b0RTYZ2UEIZtBWXSWGysSRQLGbBOU6fb9nl5WkVKjPCfbrckOd7nW05FHKCCeS3cUh8ILs4osr594ti/yb2gOm5wFVeFltpvu/Ojp7Fa6jAPSDIehV2aRdSI92YQEaagosCrVRDg9GCznZ4c7W1n93oASyIHOyD8n3XF2oAXk84FOVbQvklpKPt2ASRmlY/p8Gb41rxC/ZEDLxpx3cZHOUzr1Es7c2jG2OKTFFauAebhflGbZOSBryUDamjS5Twu/NvF077a0vJ5pyZBorBWYS5Rb1AjXuCE0JCa4TQHy542tlll35tB1eW9FC6Px4Qernh0Kkq+hJ3m3uWe867VaBcFfsWAaUFjtF2QEpFuZIswPveQIeYCcVL4CCZZqctMoeOXAgdM5KLuGV9Xp0r/aVt/T5bFwBMIjPVFtSr1kT7nvip44jV0/SmqmgWtVupfPqyNJqA7EVqpjegNWcJ/R2zCg7/npXSsc4cGQU47cVz9sq8qQAQmZwRSg4Ow0vLfQNzQwNpjEYu1F0ZwQFloW9O56dvdg5EWHsJL0FrvD2C7rgeX609NWSNSD94VJ5jxZrIw4IFmjARpp5oaCrxv0bHCygTtm1YemZij6KqyAKW6o1he1TGACJB0OMFVRFoDdxaYFKDIYCazQJY66nf/MhrBuO4HsgceaSqlO5Q/P1XbcUbzx4wc+RoE+S6qw3DgzBiUBzoMwgxjuT/k3lyOTShqvdwi9AgXa/IS5sfOC9LXQf3N5bvhNOE+O4Qd4M25YKz2gXm9h4jnlMwZkVFujrnQO4f36AMyY3aXe7/ulaY54RD65G7TiXwsMH0YQ2wj+gaGgEzzLjXCkyQt90ZuCqC8E9c+XqXsXikAHYWDM4Ga4YkcXsKkPfre2IEHMpoI3thgHI3PBIxxCQ/UmoMJids/K9+jdfCfzdNatyv/5xpdHqX49b506xEmFMSROas89a8/96r1KZv7sbvSZmmnrfDcmbpwmzZV89O2eLXAtdYgqp0jHlaYVUat80gm7Av9CNdHg+v/ondgsdTiFWXig9mSUGtGenPMs4aG892EC72CvbCyHW8ID07iQHWruRr5iB+l93qw2YHi3i1qIhKIfHIyh2WpHHkFIFpU6lZ8lz7xAuTK0y00bsjq7nJ+iT3hvcIQRo0PpLuF+SeUnrU/d5mGQRm2bXvg3qlumacoRn5LWow0PE8gWFOM0hLJGBE4nPgLL7KFtTKq5HSRy1FFLGtANFNBDy17Ct4lUrLi9CFIxDJufuPDef28FgZjG5Q0wEqejLYiHdTvoBbvxBB5/P6xnpTnidLiyQMRlLRbYUM+/zDnO+gMb9/2ysEdfRbJ8l1Np9Hh8uyxd7OWOqnUpTW1vbP1gybmbvApE/jiHJcvUC+EdwGxc+abBxXInQMWg5z1C5CoDSi3qRONpBSX/Cwloi4xeOtEjENpVcbEqJ+5txEsT5k41x7X+IOXgxN2S4p827xvWJWWmEdE5K1J/LMg6VdAUcoM4omw+jhsefzbbXseAoEU8goxTMMz0r3VXiEvnYQr9uAgGqDozv1Jdow+MAy6+9XKm/fqLEfHEjpLLXkpNJAuaGim5B2pEIFtQ7mVlnZcdgPbkzj1/1sicqpajorUMPUuP6i81rHY/+HSoTfYAleuZNearLXyAJ7x1c6Yhh0vD+TF4PEw+cxlnFRwi6QuxA4LPv4EbuQYM5eN7WeGGulUvQNiO6YLvYHigKpPQ7FiVH0AHbZXR7jzOPgsSnuDsCJJ9wy+dF7DPdg6g6Id6H93qXWTXyzIhGIp1NoMKXoDixBmdwa7iU+etDqVQtkwyE9e/8MIN5uBIvFcZ70SpV7QWDteqz7Wgs7aAv2om62WPKctFPo0FWizUz6kjddbGD7KRzicxjAc0G/2H1tfbwIBvtUXNmYb6Lz9Gg1fYjCrgiBlkDYKNFlysjn4jrtuMlXnbDg1Rs2b65p8AbeDSqDBGsMjd/UXm3KfjKZJaXtX9uzz7CPRC+W662mMgC6AUGPKZL/dtwTUhR1xAkzkSapUNKupE/f9IcPKWLMQ7ZHOlJxvzXvTF8Jjqt9ecB7cDuO4h4vs8glGg9QUk6MIx5SRMPKn6FKEa9zgDpq+2+iUla1pn/MQcHcfR9tYB2rQes2ahuLN3vrS8cRaYVG49D8DLBdn7b0LJCrbMYhMJtIEUrJOVVhXUjRCzjvZuWj7eh3tvfHL/ixL4BA+rDLG2w0cu98bvXnulZCEgou1iQPBx6YR+bLmS6gYzcENwjaskixqqMHzPh/QljCOhcT3riJubfd1QVunyi2705MC7Q4M4g6Lt8SxCCrkj2iuxD/jtsy3t/PWlyB+sVTmOjEUp1Z4buqx8NlhjRgNXM37oGhCFEglPbsqs6vXX87zm60oj4CSNRX2xrBoDzVagwUJwKHaqjr6/N4CS5BNK61dty/u1sabimbO7am/+yuKR+r5TSO/l4MyVll9YEhuGlcMSvSV37sJZ2pgIyQviWpcD/wFRKqQeENJIyS8MHL8V24Z8L/QEAJSjFTk1IgxKKC2IexSl3BBE4d8Cl+sHU86uNHfXy3pffN9IHflt4uEMC2yRJzQT1Shc5ofm7ir5+3UOtq+ipeHIl+p16MS7tvX4/P4hOsexDLvUzDaC6/B4g7MXv7BJn+i2Y8FfqcJpQ//pKMqDqECDb9SCwT9o/qfOm9TCdnsZwEPnNRXZiL8JCB/KJy5DgnLr+pxtPeFH0N5BoknXATlAVcgOVQMA+AifpkvmLeylv5bgmwxw8Ji7thYCHujBC/UOMpvySeGl6lyg4IY2gMkHtUmuA6P2rhoNR8d5gL32li8h5eUI3Q/7cJkoprP+B4wIc+ncIQTKDB9WOXhGV988b9+YOe5ZeZChn1yKdvm8JtZ+8t2CkLID6Fl7GWAxbG8K+8mwTq1x/tYdZGflgiUXzq5JWQ1+Dca6YBAInEHTJsO1SJ9B0dbHJZRXhHhXySggDmoDmP5cTwH5lfqTbdFyrv+PpJBpACEXZ+gCJU1VMPip83AkTmQWe5ltiCM8heMUOEZaZci/XIGXH79J4b1Y+H/qSG9+mt9mt53av5TSSVeY/GnYy/eMcQHCr6dnDts6488QIzcn2baeMZslKURz43k+8wHGgCBlDw0v8UOusS5aSpSHf7al9boGAouKiVzaLwKvMes1i4+fyKpEAHJ0bM/zqYKqKzPZE1AHoFcbwNPbtbj+UZ6t3eEMDpe/7KMI8WkzoP6v3HsLLBFcepVwY5WRcXaChpD14gn9TGsEZ0bmEflje30As8DpaPXBKSaIJRvDefuBYObm3LyE0xZIHLISqUeGb9w8q+U+B9vp2u7a/f4pVnHiDh62BgwFlbwJBjWsU0cZTTQv5z1ehuJKquZpkwf96VGm0u7+qLHr/UiRNweADpINFgvZEcLRqsfbS5P5HY8MWHkQlOplizpopgg2de4atKtfXC6q3gsFVWJv4wnokxa2urlgleUuxNE/kOjZhTBlIwE+PGQBwMz85kRDLVYZCCAi3PoO/0WEb4Wx3fH+Asune6YrMmGz4Hhk3wV1HTzE2+1j6xiDIszjmD0Uq95TVAqQWD6sBz+Sww4CJ2F8N5dhodvrPsfpmoEZdl/lDMtuXFXl2q1XdJfv1C3PltE80IVwbu9opvocY9C95dXRSMGPl96PrL0+eRpcxy0mlCBLD+fujSNbS2ME8uMId+2I494t4eProErDzvKMKRXQia4SxTp7uJcMCOlHkGEYDw7/osgX7l46ht0zQaw2TRKRknxz1Tl2dod8M4gNAWCePnuSLTt77AZfq9vyyzqY7+jDOmy9gVCazeaUAeLi7lUYW2F1SZ9Ekws59PralQhDPz/ZmFIPT2GXarZRQIIFIqFY4NWutN16IMm+eoCetMCZDxXf7HC+/AWGOzuYn85gilx/W2I3svyYF8mxOkOjADcnQIYImyI+l1umtILvaJdMf6lfRqLG1xT/Ditp6Hd8A4Zpzm1JJ7QDwS4qz9IRBJWP2qMPhw8OhMYTE3vPN3DtJ2VccndwRTQ0D8xNdgFDb+ST6vuzhGo4G08O+DOxF/mOW8f20u4MMtNDjZbmYRL1PY1Oi3tjEeRTKnm99Fz70/ldCHsbQfVGruhxpXQXWzMDVXFpKE/j4gpN65RRHonb7aOV15jvt81TWhcUZuST26YCKWpDpGZY/rXdxk7DXK0EE40fHYb8vxb8ib8khVuDQLdjii8D00nXjjwcmrPNUp89jeB3uQfsz6tyOzNJjR07ZBiNJcuZYsNDzUsyul5sYnUBohlR0xeWvkxA16uRB3bj40DPYQBWY8SdjRFbUZn4gByzWqLJabNGXLHiv/WWYibnG0r9kBw8ZbdfD+zhNB7Dk7Ytp2pb3LCI4M4ZF5/0+Eu1FGDGMm8q8qD4VDoaCeugHYlPTO7r7I+ZHd/of3Qwe+39ocG8peQBND5E0UQqJIbojYXHpxYBvyIBHpZ7pIhDKLumlUexBydHw5cZGMggaOAJXTeHPbZl9IMYWwejagHl717Il4D+OBSppoPgTDCw3ttXjX5Ixmr5ZKWwY7KDFQwEpdOlf1GIYSZP5LR4xbOoZuICvw9aNB2yd9iJuTLtZ7giQ30OZ8lcR6bN3MAEHr40CcKPngfpNNR9UineTzyawdTX8lQg/6EaPKA/YQncLUbZuN8366FkDD7CtGjYRBHZTTmawE9oI+SJRfrKeoIL6qunGO0V1a8rUZsdVEPYNmoRpQzAM84F5s8/MkxNvd1yPvjCuygxRZIUg3HMS3iEAASAcDs+q4fr5/7ecR5F2SiUAmLQmdDKfCJsAdv1Q7s0ymiXspSJxC1rlPDKdPHawr+/NCZ8lKa+zbPzNxdjY+svEFox4Edxd+XzU6RHIcm8Y9ITkD2Fb3+DDKI5H93cb+tppxdZuSTkRcEi4rPUjw2/zwi2QHxXaBQ3ScBQQOyDUAu+hctx4nFy33eF162RRP93R1PxBJ9Ba27Z7OewA51G+1UBjBeIWasPUvdDVkQbxHH9O6lUWA4n/8sXcDi99Dkr8D53B28ticmuAb8KihOCxg0hvH1TKkX7PUHhVAZ4ZY39dOIORb5X1EMRXJ4MtGkgCPQBmlEM4dl7XLK1AZJ+9m3L55y4kC0haT8CZEiITm6OxCCnua3wsIDwUPRgvR2picyepn3iZML0+VTNiolR9Q4EdCtcjRiySniFvWuqkf0ZOv9gZ3Jyat7fuGb95Fj3GZ++t7w+Bl8IcUKx9ycGlUtGut2lm29qf4go0WBptHzqG3c/y2KOOfQg/+iRgjzFmb/60uUIfNrlkUTGePN6JmiUIr4nCsB6PEnRtni7CHfVjmAQmhZTtP0qQ2q0aC8JmMNhAF7J4G3F2yPCBN+fVtluopRuWm6EZbvnxHV/m2/H5jOmAgzd6ZbvreaFLi7z4EqRGdHrZH7xyvE0Bw83a6/92cN/SNCoZhGKM0sdwzBBTift80WqlRxJqXewnvnpGI/cwZev9w5tQ1UNBnZlL7o2HjRanpWtDAWDxUjnlpgq5Cc+UoSWDKVI8alSZ3zSiL2QKljRthZM12dOcEuzxdRSNHOzPK3I2KPp8poXNQRLTRVJCsUiDCBXsrwepBihbg3i3WnC5SAeq8fQXa+zyh9QruGoNowAr0FDHHjN/ij+eAaDzk//w5D8tlmovKdUo/5AfFzgtYJr4iHvV/wmt6Q66Dv9mVgKoXqqaeklVUuZrKCAuJuArQztwCqAALBBV2WRthr0Qzfzn43goyV8VAJ5ege5bAjHRBLNsdOEf9HqKz2sfSUM4cy8nqSn2VcBJRr7KTghleg6LSlst1mYec3tOlm7kHt6S8oQzyQOzZA+wUG35mOKbHCbB8oXngLxFg+Et3X8VHvOzmCHynwJyiOIUWO62lyYmT1WS6m9FNZYrL9if1Mf4rC3P1fbWF5KzkIVyA6Krr+NpLDnLHSAbxMjNsRLUOcOVk6qV66pmxZe5fSXHRJnbiP/RGWqL5qcBUgIOJE7uN62rcSL+M6Tk7j8Wuq+1NtjOHbbZoUkr831oWbipTMfTw8ztkcEPthuh+sfilGtGSgza0yQIYI+C1Xj6IK8OOhhSnPz6/1A3X8BGj5jobPTE/ZW7yp+a7g+/tMQZrNwnpeaq+G8hylc+Y0VVkJbSafIzjk9oORTkg6wYclBN9oGodfge5+C5EUhQpPPUeIcQj9OV+kCPi68grbETRGdU908HVkUlBF3wGWQb0UAQBe/C0n7NmF9zqVugsTnmmRH+0NLXS4tNV9AynFWP5/9KCfSDLSFS/YhU+wXDZyKKdOEVB+QH4QyxuCMP3JnApMpROv3M2XOV71D2vC9b92MH6VUYbSoMoswOZ+w7A1YJs2Zgx/pUzzq9kkfGFAxQWHEwQRYp7HfF5nktHL7GGwqbbzWK2/IyfeTM2bDDV5kqukbVTOCE82D+dA/kALGpgfdKURFBhAmd9dLaxjyDZLyTvZZ4AmEn9ZhKVxwOPLsIekm7BCdwyn7QNFsZVlyEiYTSfaFh5QjbEBXhniHVefs0gmn8smPk0zmsRx8VWJEn+o3ktPdLxqWUSfE3ZTBVgBosCmbBR1lqIXxSwEcGzHGLig+raEVzmIJ8bdK4VHAkYt9P5JyW/ubK829wQAdyVFJXj+///YOrXxdWFBoHiA8IJ3cS8aVf+PnbpC76TtjoxXAFds5eJP7nPZLDrs9lJ/O6TOlb+LplOfyDoPXXQZo0Nxscs2hDvxSKtvKleRHPA8DPxC72d6JmdQLI26kbf6C7bCaO7P5uWreJr6QwMfk86/ImT7kjAYIeE3mbPHz2dVcKfgZKpBbzPVWVoAtz7bg0IQuyXWiDgELPl5mOkkLKtM5HwhvKbgr/lu1ScGCTZe658PbSRyNcE7aDKhfaA1PcrcPdekbs0mfVVAyOLXn9Xaes0OnoJyFX6rCj5Nxqg9mJtRbkbbrfFbHym5xnHFNg8ekda4a21IJF7zEc1yjwAgXcmC/7wixHpe83DRepRzOOanlt+lf/UKPcBoiww4j1JittaxJdLoskbEj+mJfoUOxl66zvabLvngSbhyfcURJgn37rs0zhsr0miBubD9ko8Pq6EL5mKc8a35674nZLEyODL5K4X3lkcyPFCrO9q4toUo5dtmMVZfqAOAl2O7kfVCfG2z4I4eNrO9SBCGC2O/wCTfbO4CMaB58fx2SuRlWdS0+QOqgHBy97Nn8FKgRkNuttOZs7I+UBrKoB5cXfwDLUdDqMhvvt9vZZkbgGbZHEcdz/f60aZZErMFjfNDr5/Jw99MneDqKj0KN1EWnTThvTCx36RSxnyOPePP11qLABoxgJ5kbDNyHf+uS5cidwg2KkPNV+clSqTzdDTEb2ICjZz/Lb3saQ/6ucfoWjh+OdYV3dlXxZFZZnXHNJoMgAaL//wpINX76A+7QLipp5T5BLh36uh/SfIAmA3gR7pQVvKi7anZ5KE17DAzX95MxVlxBeupWr732iMhqm41p50ZNkRotrAjV+EScrrGDSm7vb/bXmtAmx/Pi5nhHiKmRdpl1K3vxju6rtWK11LBY1oUo46X3ToTC+iTzkRPqX18Tk3emC0opgpgpOrzWmLtX4sXCi6F9O9MjYv3nbPQ/XK7sMAuJQzwCgGudeJVVlk3DtAr8l02BT9Nx/IHKpqxrwad711+aLHfKm4/kl/GBywSkIkuW6Dnc0HdNcw5CuxHjHe7c/ihwkykDleM7wZWt2WPrgjdFtIx8d98B7Hoou9r3LNq0xKqudoYbjt67jy3aXeI/E6Sufb7mO1iYLIrz3xtEYJXELxein8iAtQq7qqhb9IlSQXapJ0SAt0xu0wJHBhSRLRFs1//2Wzo/ICcFHFIL/E/E6t6dWp23FuotN1BAdNdaE1pL+AaQ4ccShmaGedUYDm0t2MPQTZlE4MZvxBQSCecOV+czMw/3uFoqfW0nKkLK0MXLFFSTrqpRHgmupYIfh5aMo17Qlgy0zk3AcZ9gjNnPBAm9YqSDx+PS5L8oH8uY8q6WJNUw4vwwAVzzrDxTibmlYtXFg6qH5x11dEvJBIH55258NcVzrn+fcb2ECqkIQiy5Xb/62SShKPYosCle7YBuUhXanPE51KTNZaoh4+2oLIoS86KVpdRAeUp1iUmgmpiB3+vZAF71qKnmlaUkgGlhzmGJmpDyN3hhxOtWYeKMRxu977XwlT+mEvKJVfbODUm3pkJlPqd4zOYdRghjFCh+FxGtaK1+jMgIvxCQLTlbkrhoCuvVPh9gtInouBYG/oAc1WgrE+ZdV3PV0JkdOc/sVf7w1GTLVeqA5euWs4+v40nK5JMwwruZfNOMc4JWc1jBT7GeOeOgdRAu6R/KEhst7ByLu908YiZbcolT3XNDPraeKyeTWGZQ1DxWGXdPKWmmvl5NwlMbT8KcZIoJRjtyrpg15yUsXk1sGnTTItAm4JMleZF9oZ/vBPYbKTeG3Rt6zm61EEtoNZje+tTzMFXlxhn64CAYb7VYBfiu7MRewgJBkItjwpaG1WJqvwF7Bjiqd1oqBkQdR0427klgSyCz3v9Lw7iHXbJ9sFpYmjZokTpzc2P941rOLipUlJjbUnrtCugLoAqtiCXWYC/do805A2x3lG16RQrZS16ISk0aZH6II7udvyKi6RzkvbNVzGDj5jK0s4ZEuG61GPKMd8Q2wuQbOpKSjPOTcXu8+KmJBeGtC5JZSF6DmTPhSHf/WR7sX4qfuERGV5nV664ugldjwou7gyQPzE7g5NmqpADGWuucQkQZSRwC3Ew1qY0FJ7jcmh6EmSgTAsy9vSTvv6cyPFGePYnTJe+kqVX2xykDK0Nrs0Rcd05uKiaqYkDKl8NfVP4TGXnens4qrnahEwtCu9OizW1UEg+47gkSLa6nZNswak0F3qi4NpE+ToFVca0KF/Agly/XLJw+YmGxYF7KPLJp+5WoJRe5/7Qor+1DkQrBJWYecyWYEWia+8Ypr3kHgwdV0gYwcuBlN/OPBPlwJTUHre8mQjgFC1msgQlTjodPowddITlt3zQnZYWFa661ptuovfbChzaXBWGlNd1wRHWVWi+518Nbn/rjkj0ful0XVV35diaJc/TYOtt/tkPMs81r7R4KtC8lm4RuSj5HcjXtgfkJCpoWD3FVytN1ezYQO/99udAaXi9GA3COiXbhrSL6IlWwMtlG4IUjjCUQpcfJpEJg7u1bg19pUiNoXMrLyxaDiL/h5FN8jW+0vGsiJXRXjyD0bPBQ+DYODltWccVWmI2+Bz7orRRAvN2H7Xv8xtU/DHzBZKkJODTmjuGZnVfz1RAGe423LHa6LzPbVd7cUepkwefza2hJB7H0ovTQiPooUOrYTnN3drhytLsJtmkNzvNi6uxaOF7FYWJq7KgWoPUD4BLqdwndye1RwiMHk+soqNZ1jKgLlB5EQ+yrcGMSX0jqXr1YNMITiSEN0YNHG3vk2C01mo1zdhk0bhjBno9q6YmIXw8ynG1UhzX+oPqwQtAHFtRr4kzTF22Jt9n3FPGb2Z3NrWKMXgQb4QN7XIiM3JSxndIpHSHW5ldSSDUqc5um+XdpvyuLUs5oqyWuztpNtXq6D9IHMyJ6VKAcPJCYu8b6SFrYe2eryBbm3bMODBgF3ILPwPtzSyA3TN2fcDBgkU6/I8esS2iC707rZAyoP+a3iw0zfqLvtrgIxo6sHPeAmg0OEQocJB7sPr4M6mRxJEvna4E1BG8kFZed5DiPDLPW8B+/9Jo4nJrKx7RQx7akLJGR4GAIliPLY4GXr1xsEhlJP7/5MU9f7TXhgon6IbU05i6vj/QgYKxybsn2jZ9Yc91N00twrjg+gCa6mOI4TFoYx0oWeXInk8kzSOgxv6I+Sc+vLuB7D3LVtDouqPKeSNi7w01EakQE5YKSBDLgKpD0ZKECf/8WUGiQzn4q50p07YrWAYol+XNO/lnqrvOl//obQ/zZ2jIYTt6PtwDFhpJU6u7ISWc2rNQ4et8x2GV/lENE4nDiBVgeQ3aN8P87gQ4nbhZJ0XpwB5Hj5vPWlRlnHIMy0Tqxe9a9vEwYUm0JH936H4QimNMZCx9lMw0VVFIHKtTqoyryH8waTRFlhgbtSzL97HwUP+nXtoZjh8A5/84pEeRCp5suCaC1i7+4b3TWmBVunfa7K5uUCgeiEY7s/GZRujKU9aRfRwSm8VrxFD6+Q0lOKhgVffNVBKcCUspjX2pjNmkSgUjvKRo8zbS/6uc0o8yb5vNwkyNzs0gCWsSl3OtrHQTiBT4oTzi3PREu/KAUDwpQrWr3I8c3Ljrw25s/tZR7SBsco0LDyKgnmattesxsVk3jzA3gnIqqV/Rbba1xuFCkfVjOmVtg8U1G2JY1y8ss066tIc9sh7pJ7umse8v8NdL9jxVOIhTYL037o8YR4bNWEJaH4wFCNqnMh3t9qOnCvvVUgRrNLQgk6ZzUegmP3dwIhBP9VVpwpxa7Y63yGu+p+FxGZho0ufuhIrBRnJOv3wBDCthKFEVElhtx6sYBndXmMm6RppinLKAj4FhMykeeL7GMrTmj54DwyuXt0DAfFSNdtRZy+JQwtDFAH7jdf5Ht+h1u63NNYQ/fWgZdckdOsvAprTqLnm3tk2DXmavpckyWr07ZJ2PSKX+h9BbFvlENd4P1ircUSIy2DGg7dyvrXOcGDBm3aJV6SRozyWqp7ctCXlW3xVbyhNyLnyFNOnxhrMee5O1BYtpd3sDBaBvGIVo92ADrKhZ5rNhB0vWqJFqpCNT5Dd3R/hu6MJHa9fx1LKsKcerqyQv1jh7j1LyYEqt7EzTKjJ6V4bz6lBExegug8zXUobjQ+XTLB56Szbm0UMym3FJZOhquifUTGL9F+jejfRV5f8cjCBG+CDjIId2Skt/iIX1ANBDqfczIpV+SPwGBgJx1magtIwGjF4nMMGpAjobH8s9+jXC4F6MKtW0txHrnK/9y5xyB48ZNyoQ6KjzXu1iVWrW6guOcGfafk66RFb3phDbwAs09H4rQxjSdtQpXzlyCrCAcnT46Ndp/4NeUep/KZFcaHPdxZuQlJUMgwtQBDUbjzoBjn8huNBkEVWvXO5tPPjh/gWgJi+LkTRn0l+XC9OsAeb9QNMqg6c+8I3hXvVskwAhvfY35yJxUr9vGQHHoHCc+UP8lK+maAePvcsbNmnxjrRScLmHA/RS5AwehDa0VSb6dCNdw2z2xuefpVWzGgiWR4IK3i3FF7A7z/YB7vl0spUHxeFJT0+0v3UEoRyxHJyCee04dQ7yT7fHp+aDaZHBCFwCm0TcWUxBNnefjg9y6tyzep0ispQwyU/qwVwqWWTJmfy7RJqe0advZsU6IqXwGIitMQBCCjL3Ny8IphDck98pM/ooLIjjM4yGu/qrkSbzAQfXf3GQZP16rH9DZ2kbat6xfw/wnrr+bhZb27xNyNZUojKH3ZMOAwvuj2SAry8NymAyuimQJ9Xm14xq0/f/ki0rfK2gyD6AOfVLN5i/DvBCGxbVH1AcYoKY5BwHVkcFO3iAL4kdW+sxhREyTmRMngrT+7FWGUCKVTqyrLCsBPs0ntMziaSMKcSyXBf7Y7Zn6+28vkUPTFp8Y9dg9/NKx2hNyYTetgqR79jp+5v288L42G/wvCMZjgXtowNFeTZai6qE7irhrfObOYvufWPl0dEJvuTXBCFZbv8Qh3IYyzO854m8kynXFTTsRAKrOzs1T/ISbcUztdT6wytBPj5V2t+MXJ0eQmwJRGvxIKScXoD2d8YCYZ1SyLhOusE1Cb7LRv9bHz74VXwL9RnaK9SOMHk+mstmK/T4mu6o2Xmxv3sr9YI5s3L2BCQORGKcVJ5hqSeroC7GaHEl+qP8UCR5cTK+Z+bT5EvODgfKYg4P8dGfOJKsViDsO2YyHFiu7zvKwuVnX1cr5g7jz4reW7Ze81JlZkWxCuJZbNCowSIt/X0UMWs8kcfaSPNRS3EjXu+ggE5T9d8I8spFVj2oKjKceaxRO4wD0j9jjzNGWAMj36QnaV8goIvJtqTok/I1q0TUqanzx+7Zk/3RLCtqFFitQHWBiMwsstyXW2tKVXDVwA4oFDcm6z2FJUu8j1i6wkzqqveoT6gmUrsgZkR3q93rH8DMPB20ZQHXcp2kTEsf63aue+zXhYG+692g5awxS2vHQBZ2rMApIp8mkyA3ZkOR4ljzqd0szH/bw/wAOXWG7blHFuCvMretaIdPx0/4ZkScN2V5CUvd1a6BaiR5EK4sf8TDQmGUygHOIjssHnmRqNoCGMCiT3EjNJ/JPeO1EX1/Cu6eQM8mK2jPZkqV4K8D+IR/2EGUgI3HhfqacVG4pw7t8rtGCGomPEnfy/4B8caKxZf4th5bS4flWvhj/Y87+U415PDGiG5cBwfanaj7C6b1ahvonJpNW1QDnYv/SNsg9Mj3usHPBm9vgiKSxOlEjNsluPaMNcU1e0mLvdGRUL9p0eylOzxuqld4ALmW+vDCjNUOIcN6S+7Z1GIcXsrBk1cl7JLqBMMBlxDJuDRAHXeVuz3EZbMQEZyTBaxhpIEylLxhA3f7CNeiRXc4YIiQl3Bvz0v1pRQ9cAeFD0kEC54V1klr7dbqXxEA9G+tbHDBXvHaRAdQVRyjwLvkxpnZyvXh0D+mGEERsnsuEAxJscNW4uD7xvZE5fgIwbP0jY5maCkzANavPWK2GC67me+Z91TYBqpr3tJ8z5eZ+sPMybI+X/wbFIXW2hrq1uEBn+y1z8jdkjHRAGZ0b0uyjtH4FrKhYCiXzLn+5AGZABntR+H0mTj6EpXPmYUdak1Uj4Y1CqcamZQ4uR2r3atBL41xS0rGJWjX09Q2zKJtdd0ypcj4ALFLkXJHc1RydRogro8cuv1t/0obMT2x5ek8EJbbqff2whrHQL8WL2vqZeF74JijS7YrMg180O4RD9JddEo/VZijwdKTei5mIBC9YnNwyOl4KHt5I1rbaL79gc0gUWRArJABr47CHO4sYh61VTRryD4IxFVsYKl8hDONmPev3Lg1mTJmh34HDmAnOOcjFf4XuLi4CqwzFGrwfIOpg3eMWNhv5RMyKjAO8USd/7SGhzUbQ/lVauZoKoTeb/tMGH8bJ4QLe9lS1Q79JIyKzI9hMSavzb39GAKMbnHNbN9Vr6pPb5rjnUTAacfWSJ1LRfD5YbsDNOlatKfaLLZpWueB7O9GXBx+7NLHPdmq2uoWkKJhQBH/hk+HDvLqzGUtzYaZeXlegtzZ3icBH1FrSKho5Hv0XbEKlSqodVBDMhjfgQeF06Q4QY4VL5Lr7kLiJxauT5kL9rd8jOWjiAVaA/jAA3ZYTLYjKbl6uJvQKoj1wk9AlvUVdDRibpZE5vTFESZfzFbvytKcNX/MS6Owp5LRaBrvqNm3S+8tPqkyBTNyCuxhppdcJmw+HXiDFWcJeRTonSvMw31Yq0rTZIFqxNIjVCH2TBQj85OUPTPV7NlymcndgS1qwTC7eXWwFD8WrBpnA4UpX+itT/9ZSjavehXGp3j+rbSMkuQ6Ev1YCneVzCHXtBLkhEF7ye4Jg7Wp0z7Zn1ZLgyxPL3Zx0jn10g3rrKL3BaHkong7+Z4CDAdPq2sCtY61SLXS1Vjr9vjrVja4Q1txknXqm+CWyducw9+LNIsRKXi1h8AitSyZTl0YS4CwgAX0Yet905gU9sIVoY11pc9KU3eJeJtIm1cchWtfUDlTo+9OGSJn8eKl9I7zB5PPh0x0i4XDcB+oz5vZTprAiN3iAI1i0eQxxYf39V9uIlsW/y3grcnOK4bZJG/YSLSFQquR+30kNUx1scM//TdvqzRfAAOSPIJjFx+y0wh8YAtHHEpG8bSsOz78c1UzfKsP5AAIAjkPby9o87JT4h0NpY7vv7no30t80V9VKSRQD2KBgNEWK0JRcXF5lN1IFOjI+jUEm/buWriTKgsVRdze/t3t9oEOfx2/WsRtIWZJBwQUbOrGGGyY+UQWcRGx8qjce21zp9ed/i01FZ2WZhC1RcsKQT8p1irVORdMaaloWogV0DomV02R6Ao88YHgKqOekSiKLt5TR+IMcctlvFS8TEbcIjEOO/1Dzhh2jjYzCaJ8pCS8z8smebHGXuSujpIjGOY1fyT0oWacNm9brD1MVg4eInU/I1m4aDLX7vrrccgH7rqwabQl/GgtFZZsLlBcgSdIOjN2BBWSNGgqtc9p4hLJd3GI/IfdgmnC3qWTvVX+gtsSN1fqn59yXjJD4Ii+1q+TdWWfx99pzwqaw+db/rmUV8zxu5OtxJ7VWZzgrZjEWzYaLtxxB2bWbB7/FlWlAxqpsDMhWjzEkwxqR3E6huoOB/HQFhmivKU+oB2yOIM0DbLaU90h1tkurQje0aw/EkopYpCSMxg9VazrusuccHseGW/gp4oQ3wDM5Y/zQxfRc7xetbrSzF65dzJgcFfR4s2aq9CvmZVlkNWQpoe99FN+HTGLK7+t49V8NvGyMTd8KszbG4eXlaHznpDj3Ls5SC79akFzucs/ZmRSHGVaYLsx9eZ1BwnkauA9pc9X5TpwulU+IIa8gAImImpGP9K/1SjKSfOqbQBzGcS5c3YMGa63g6N2PpQx8/ylP4pyh6uG9lpw2MVhqudGyn4S8UsRsunHjoaqoQol19MgBtLWW9rvU+Hy1chrXkeYCOrXpte0pL/ODgYD9CQ7tqjxzFicZFZekoYRultTnQ3shxLxSeKxFKddORKMj8xmlRtLg68PUobGzzlM6kExl79VgNCEZggAdzhfbdZuuFoBiLvl+nITFqV30RgAXa9tcvwY952pv/25Fh8GSXgmBNKv+U6zhjmPMJirzhdQDGRrqXW5WjUsoiMrugiBrsfN5gYazRcyqdo+2U4G/YIXl5gWCdn8pqQn8db9aoyMw29gQ0xUIeFNj25XHZ5Hs8vvvEcojvAaOZLPxT1ixGlOfbWv7MiH/YlBdo7c666LFuvA3YGBMV5rp3+Cz5ZDv09ynIpGfn8foXuN0k5id+dnu2dPLYq+7JdxFmIaLTiIOXJQcaBVR2RXHnqnkvq0Sl4+N3imc+GGWZkG7urtYtDCgtVJJ1kyGoecvVRzNADV8JKygSKDRY+M9+edk6uq8zgy6p7cVqvdLAm3FJRaVK1TJyLpWOJCwjKTLRcdT2L/HgNC8iFkgzWR4BRehBeRWVhqm8zDm56q2lSuOjfKd6m+jZViG2cLMZL0b2Ik0g1I/JlJvnIeob74tE2+ZwKamKPYyTKRW2aTHuRmJCdK1ESCUAKkMbRLa+uGjM4TfIRDJwC3c/cSiLnojTWj5LWw==\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"Order","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" →","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design →"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"db2RfIwt/EgN7Qw7W7gyu50pUYtgaOPhhUk8C6UJ85BEFwjrBoRLCVYHwFKlrlvbd5LLU3crSpUtkMwezu8tC7OELMqMgl4f77cvqql9yCqIwP6kC/ULuZhqeeBcgqwGa+QRh1QuTT5atClVUk3/pBNRXF+tCIKqQtKRNXb/R2HRcHn+MkmmITmLw/csoSsA0bBdsbUuf6F9FtUM8cbVWOQMbo6lL6nevnqWOUmKXqQeuwWCaNH+mbmgu2gGruIZAiO7lS1NmcMx1HblJj5rwhvxfbAnWqUL8bc3tPYQBT2Jwtaz6GTkGzXioY1yK19rjyXZQcXI3+gomGjZvCSemmSXjekS4II3Cp1ZFw+Ub2QTfk5ih94R5nWUxIdC5lbXyVQGkEVlhh4mfulIkwuSvKAAhzfuFeP0z9GM8ghItDrRj0QCERiukneezpU6gh3R25N4pD6aySB3LKRdMMWTIermlT96vRnAGl7zREFzeoAvxZ/Cukt1zj1hClPbz0ZIBuz0vHzF+Uaonbr6PpO9ZNSvUmDl8VBiqDC010js6ywQuRUABc8KmUVGuKXiPoirKTNlk8gdl5mis5hkPfAIBlKYgLcdMa1Mpypi9VAeVgsGxGCKCmdrxL1FwTVwz+mefenlsta1hSpybw5u8yRkdUFyOI2Aeahim5oXNDeIJjxL3xV7CGxvlq/2/ZNte7KZAfSWg8CtjJumFx+G3EaQlBLL9Py+ereiwEy+9cWZX5AhsaPrICf+gCxaODregS8wPK7CUpvtjG6hcCN57fmrVSzLEkDJm6jCADghImUrf8U6khZeILQub4NGUjUa7/KWkNUkAVIZ0ibZUMs4TVMD5un9AFNcqqnP8zoP1Li8RebzbRtTgXoa/BQogHCDAf7jFBzz/YFxphIEtWhxnwlJEfntUkvU/JYqwS+5mEg8TpRtsWZ+IaHsLtaLSwwZ71K+rAwOKGSoDcbPvUzR119j3vaVhljpM0GTLfPt07kJqHEN7Sp2Q+8PgG8MecrdTVAYYNFKYQAYuU10WZ9KhzzOKGu9Ziyd7pexl3ZDsDFnm9jS6YYB/m2tT+JybP3MTPNv2GTNwcf0A0hpc0iduC9z2JNeytRqxmLQlX2myRHaEYt8Z+Uen8GJPhZ6dosxCa2f64dv7SEYJrW2+d+AfYX2mckOP3PbQnLqJaEnKUMtkgDLLrkz3keWc2MsgjgvEE47zpWliDu1nshlBOlH6fVd0pMbyd6Ncv/h7NBAzPd0uJ6HIWBhX5vyxSBvGP4nM86xoYyfsADF68NEYhGBCE5pCDmc3rH4QrVVeWYkUHXNGIdYDsxkvt4i8jt/D3Y2cK5KMmTRtnmdU2zp0N5LUTKOHPvuFMPbaEGA8lSEEol+F24RkFFVywB8k1k40pCrmKGHN748U4iJEHXKLDBgFBRngMd2HBfK9W9Q9sPAAV8kd3o0h5pHqdevHFdA9a+UDzeALwwtz08NRx5Rfv9ZP6Nym6xJdQ6qmz64meaXSKtyydUBJYrWrCVmd9KV5MdACbLL7Y2zGGYUvgunLQkU5Ixgd2JupAt/TSfXrp2fkCaeIwlT+4Yx8LSDuNx/q6/CiUbniZgWbZOkRVhVFYS929EI5kYif1/cz4YKwN9kLTsDJQFryJQl6OEuqEfNscxeQtR93hVIqEThhuORd/tsJwXoCOh21rZyB+fi1MVqJ0G/MOSpuB0smIOk8DY5o9gurCO8KGDPNjp+lT+zpz12j20dzCtrCsdohBDL48dFgAzPxkYxI3b9RejL/ilrQ7pdPYOLstUrIlYK2q5bxOadd2SsT1935UKDUlIWr5zV63T88N5qEdsJ0EdtM/Ke/1SwvKduyasy+fbNNa17cMtRIF4t58XgriqKT1Te1ljUOb1Lq/yHGL0zYn418Kyrob5sAgTMrFGgr9NaNj7ESlhhQqJntiwBoGCgGZI432Q7Za4vbzyoFodKTK6CgC2gZWofK2zFb1QWSc5vXdQx5Xk0xvt5eQun5mWOQ7wEgljHYIVfo+NSQhnS2NiR/U3ae9Cwmi/NdG3gJpvK3fLydaKAzED/MF+nvG4xcCDxXRE7suzSObhiKrfYJhbahW8WWkXLHf9/+m0Be+nVA+kgOzq63UBVleN1c5Et02zecEnc8jA6vAsshyD6+O3WVbOxxqAo4l8P5udkH1EIFfPr1iXgEWHif686shj1MiHBcqiq5+vhn08A6VHbZ3P2B1O9T46jwvWvSQabZ7YvQ030gMmahCcgdkWLkYtn1ZPBwqpxs0J02lBG1An1kj+kUyOxu3+41yFc4UhTJ+EuYqY973iROZDN56R7hWefrxX0G/0WpkHpQUtstq0+YPNee3stS+8y9pgHqsZNdfCCJYib1IPrq0i5LsK114bWmLBQEZ1tVPHE8vZzratOTfyJg9TX3CJi7Afk2YHoHMjCzaCyOYnH3ym2vaRdzOwC2S2I06upCM+HQAqmaRMxqF/BLTeUsL+HkT52Bxq3UnZGQ1SWUDdya6IA0u2p5TOu5BFs4/A4sc7AG/Q1BWEIXbmskVWlgk6XJpHU9T+jO3PxPsTWvQGRtS8O2X7dBMCbyY99bUog+C5ljzSbPIBuIBqZKZJy/98dw4Lq5ZCBisGQi7ho4YX9svuKaCOs+Rmh2wwYYq6Fd+xH63B8DY3VsJr7Vw0qn+qUyvIzUU/S80dplg02jdY4rNM3TqFUzy1NPK2jYIy8ZIiddmZR/TlQW7KYszpa2PMPVxFrUbW3wiqCh1ffhM6JfRr22yW0L05uj871nZX2zUEkccgDC4KXN9AQ0V/WKP1of1ulo92ldHbUk1jhkN3to852uH9JkpTHniMeR/UVnaZjpalvIcexXENYYhuUVV4/gpMMAH+CYQvINqnTMcONVNR6Hrlob+ojMBK6poeGo/r5q6MnDnOaVfeFPKspRT2wA5h//oDIr3kWg+kfv9/nv+9ExN0AF5ohBI4xRrUEoQ1r/GoSn5qLYXCb+Mta3YKsmWPsMDaQrKRGz2YPA+XmByUiZW3wgpz+o7lJa4aWqenTpabgHFpzlMwlZi9/sCGPiAEKelI87jBDgMB6bSFOAhatWKHdRQecd2MNwS41st8l7WT9GhOK5LNBvqxLBeUd+5PYFIS7wYXa/j//5PFdt8/OjBx/30b4aI5u1GC1y6RKwac4HUXLuiAPW3bsD10/PHKFQ1Vo+wMNyA5yy1P6geHZSxphHGlGgXOQKhXou8rczI5PFetekzJBrVPRIZwM0kEMDz0xsbNDwmAIlwMXvyszkukS7qdJ8dRX36hWonMGbJ4xJ9sqCzeQoznytO/TkmUa0MRtCM+Zul7X+Y0i6In3qsntz39648SSjowzS+0gaXzV1ZJVwoboSefVVNp/rODgByzJTTv4+MtV6rJUrvolFH9ORQPhckIDUNFE9wiWMSENYEs67vTmFBcXIwdVRaPf2A68IhG4IPNqZb5FbkGlKdzkltOSJ/dSZOYhOeEA68dCNLmsAvu7xzOAR8reMJM5zUStVWGBuMzDkjYBm4hnANXZ6BXfHDXNMC+YZ+dFvRZXcRwra1IEcIP2DanKa/NVGn7R8iy3Qv7qHJhPZtdU+rq95KEGkZBLWfrZKwVlMtZ37yM3XF40OxGi8moNJ30dovwNdDLSVtI0ekW5i1yGXZD9bXutYm8TZJBoXo57zdVcZiRQFaMYVF2jV+HWJEzx+FB/tUwhZp0jBSiAk2pr7HGbNmV3sxPh+VIVP+Nxj3ztqbHXY0/wVcWChp3OYWuGfKhaqymkFTk3ULWCyugix1BiGRxp4+V0HVAgI14X45u4F3HSGeSzAkf+F/vuQn1NDDU4F6/qc3fgpsEUYjDXvftZIttL6XSmugIgWXmo6RSIuhXs5pOisBDJ3fc1jNs87o/6TVrDY3wlrMiIjyIt7fec3b17NtutdIJVSsjd0/mfrbMzzvnMyKK6Q34lxoLWwKIejYikfPzwSX96jDfx5Uz8WTq9nBV4TUQpb6V6Dd1xnmDnDT5qwjIpZLHwivnaVg7v/2EisLe4lx0622aTAVRFjvcI8AqqWPiSVImj80UIkcOEdJGkVvjcoC4Qb/lzMZ84SCBBeCilYy8KpXJsK9Cu1tsb3dGBR2GZ93T0Alomumjth8xMZJF8CRBsQVPEuZVwzQjw3dvleELz2cT2PtQGLA1+t+QiElEBRYrW7Bp+jOuNu0blzJbiFpj3ODT7UrXIal92iWflN0N5AakzouOuQaYoFEtJTP3Lc2HDLfWJkPI4NrVMFxD3OV08Mbo3jPNMumy3VmjcNOGtfWElY7/KrlHN6yfB43NBaYMqbArIOdnmkANMgkCxoV2xbCwz2xC9ksKx+EtKXHmBp4gs2vxlAzBIrsOV5wjVEIIwavrTvlBmnr7vENLak7usnBLPSIXYrkLOYlpY+7J1Qr0Uc2N6E+mtEtqyb/nDAyyWluOHvOPN4ZfsPByhye8RD3MjoZhPsfRYY8mvZFpDR+cBXwkA3MDDTe1ouSfG4xYvg1b0RTYZ2UEIZtBWXSWGysSRQLGbBOU6fb9nl5WkVKjPCfbrckOd7nW05FHKCCeS3cUh8ILs4osr594ti/yb2gOm5wFVeFltpvu/Ojp7Fa6jAPSDIehV2aRdSI92YQEaagosCrVRDg9GCznZ4c7W1n93oASyIHOyD8n3XF2oAXk84FOVbQvklpKPt2ASRmlY/p8Gb41rxC/ZEDLxpx3cZHOUzr1Es7c2jG2OKTFFauAebhflGbZOSBryUDamjS5Twu/NvF077a0vJ5pyZBorBWYS5Rb1AjXuCE0JCa4TQHy542tlll35tB1eW9FC6Px4Qernh0Kkq+hJ3m3uWe867VaBcFfsWAaUFjtF2QEpFuZIswPveQIeYCcVL4CCZZqctMoeOXAgdM5KLuGV9Xp0r/aVt/T5bFwBMIjPVFtSr1kT7nvip44jV0/SmqmgWtVupfPqyNJqA7EVqpjegNWcJ/R2zCg7/npXSsc4cGQU47cVz9sq8qQAQmZwRSg4Ow0vLfQNzQwNpjEYu1F0ZwQFloW9O56dvdg5EWHsJL0FrvD2C7rgeX609NWSNSD94VJ5jxZrIw4IFmjARpp5oaCrxv0bHCygTtm1YemZij6KqyAKW6o1he1TGACJB0OMFVRFoDdxaYFKDIYCazQJY66nf/MhrBuO4HsgceaSqlO5Q/P1XbcUbzx4wc+RoE+S6qw3DgzBiUBzoMwgxjuT/k3lyOTShqvdwi9AgXa/IS5sfOC9LXQf3N5bvhNOE+O4Qd4M25YKz2gXm9h4jnlMwZkVFujrnQO4f36AMyY3aXe7/ulaY54RD65G7TiXwsMH0YQ2wj+gaGgEzzLjXCkyQt90ZuCqC8E9c+XqXsXikAHYWDM4Ga4YkcXsKkPfre2IEHMpoI3thgHI3PBIxxCQ/UmoMJids/K9+jdfCfzdNatyv/5xpdHqX49b506xEmFMSROas89a8/96r1KZv7sbvSZmmnrfDcmbpwmzZV89O2eLXAtdYgqp0jHlaYVUat80gm7Av9CNdHg+v/ondgsdTiFWXig9mSUGtGenPMs4aG892EC72CvbCyHW8ID07iQHWruRr5iB+l93qw2YHi3i1qIhKIfHIyh2WpHHkFIFpU6lZ8lz7xAuTK0y00bsjq7nJ+iT3hvcIQRo0PpLuF+SeUnrU/d5mGQRm2bXvg3qlumacoRn5LWow0PE8gWFOM0hLJGBE4nPgLL7KFtTKq5HSRy1FFLGtANFNBDy17Ct4lUrLi9CFIxDJufuPDef28FgZjG5Q0wEqejLYiHdTvoBbvxBB5/P6xnpTnidLiyQMRlLRbYUM+/zDnO+gMb9/2ysEdfRbJ8l1Np9Hh8uyxd7OWOqnUpTW1vbP1gybmbvApE/jiHJcvUC+EdwGxc+abBxXInQMWg5z1C5CoDSi3qRONpBSX/Cwloi4xeOtEjENpVcbEqJ+5txEsT5k41x7X+IOXgxN2S4p827xvWJWWmEdE5K1J/LMg6VdAUcoM4omw+jhsefzbbXseAoEU8goxTMMz0r3VXiEvnYQr9uAgGqDozv1Jdow+MAy6+9XKm/fqLEfHEjpLLXkpNJAuaGim5B2pEIFtQ7mVlnZcdgPbkzj1/1sicqpajorUMPUuP6i81rHY/+HSoTfYAleuZNearLXyAJ7x1c6Yhh0vD+TF4PEw+cxlnFRwi6QuxA4LPv4EbuQYM5eN7WeGGulUvQNiO6YLvYHigKpPQ7FiVH0AHbZXR7jzOPgsSnuDsCJJ9wy+dF7DPdg6g6Id6H93qXWTXyzIhGIp1NoMKXoDixBmdwa7iU+etDqVQtkwyE9e/8MIN5uBIvFcZ70SpV7QWDteqz7Wgs7aAv2om62WPKctFPo0FWizUz6kjddbGD7KRzicxjAc0G/2H1tfbwIBvtUXNmYb6Lz9Gg1fYjCrgiBlkDYKNFlysjn4jrtuMlXnbDg1Rs2b65p8AbeDSqDBGsMjd/UXm3KfjKZJaXtX9uzz7CPRC+W662mMgC6AUGPKZL/dtwTUhR1xAkzkSapUNKupE/f9IcPKWLMQ7ZHOlJxvzXvTF8Jjqt9ecB7cDuO4h4vs8glGg9QUk6MIx5SRMPKn6FKEa9zgDpq+2+iUla1pn/MQcHcfR9tYB2rQes2ahuLN3vrS8cRaYVG49D8DLBdn7b0LJCrbMYhMJtIEUrJOVVhXUjRCzjvZuWj7eh3tvfHL/ixL4BA+rDLG2w0cu98bvXnulZCEgou1iQPBx6YR+bLmS6gYzcENwjaskixqqMHzPh/QljCOhcT3riJubfd1QVunyi2705MC7Q4M4g6Lt8SxCCrkj2iuxD/jtsy3t/PWlyB+sVTmOjEUp1Z4buqx8NlhjRgNXM37oGhCFEglPbsqs6vXX87zm60oj4CSNRX2xrBoDzVagwUJwKHaqjr6/N4CS5BNK61dty/u1sabimbO7am/+yuKR+r5TSO/l4MyVll9YEhuGlcMSvSV37sJZ2pgIyQviWpcD/wFRKqQeENJIyS8MHL8V24Z8L/QEAJSjFTk1IgxKKC2IexSl3BBE4d8Cl+sHU86uNHfXy3pffN9IHflt4uEMC2yRJzQT1Shc5ofm7ir5+3UOtq+ipeHIl+p16MS7tvX4/P4hOsexDLvUzDaC6/B4g7MXv7BJn+i2Y8FfqcJpQ//pKMqDqECDb9SCwT9o/qfOm9TCdnsZwEPnNRXZiL8JCB/KJy5DgnLr+pxtPeFH0N5BoknXATlAVcgOVQMA+AifpkvmLeylv5bgmwxw8Ji7thYCHujBC/UOMpvySeGl6lyg4IY2gMkHtUmuA6P2rhoNR8d5gL32li8h5eUI3Q/7cJkoprP+B4wIc+ncIQTKDB9WOXhGV988b9+YOe5ZeZChn1yKdvm8JtZ+8t2CkLID6Fl7GWAxbG8K+8mwTq1x/tYdZGflgiUXzq5JWQ1+Dca6YBAInEHTJsO1SJ9B0dbHJZRXhHhXySggDmoDmP5cTwH5lfqTbdFyrv+PpJBpACEXZ+gCJU1VMPip83AkTmQWe5ltiCM8heMUOEZaZci/XIGXH79J4b1Y+H/qSG9+mt9mt53av5TSSVeY/GnYy/eMcQHCr6dnDts6488QIzcn2baeMZslKURz43k+8wHGgCBlDw0v8UOusS5aSpSHf7al9boGAouKiVzaLwKvMes1i4+fyKpEAHJ0bM/zqYKqKzPZE1AHoFcbwNPbtbj+UZ6t3eEMDpe/7KMI8WkzoP6v3HsLLBFcepVwY5WRcXaChpD14gn9TGsEZ0bmEflje30As8DpaPXBKSaIJRvDefuBYObm3LyE0xZIHLISqUeGb9w8q+U+B9vp2u7a/f4pVnHiDh62BgwFlbwJBjWsU0cZTTQv5z1ehuJKquZpkwf96VGm0u7+qLHr/UiRNweADpINFgvZEcLRqsfbS5P5HY8MWHkQlOplizpopgg2de4atKtfXC6q3gsFVWJv4wnokxa2urlgleUuxNE/kOjZhTBlIwE+PGQBwMz85kRDLVYZCCAi3PoO/0WEb4Wx3fH+Asune6YrMmGz4Hhk3wV1HTzE2+1j6xiDIszjmD0Uq95TVAqQWD6sBz+Sww4CJ2F8N5dhodvrPsfpmoEZdl/lDMtuXFXl2q1XdJfv1C3PltE80IVwbu9opvocY9C95dXRSMGPl96PrL0+eRpcxy0mlCBLD+fujSNbS2ME8uMId+2I494t4eProErDzvKMKRXQia4SxTp7uJcMCOlHkGEYDw7/osgX7l46ht0zQaw2TRKRknxz1Tl2dod8M4gNAWCePnuSLTt77AZfq9vyyzqY7+jDOmy9gVCazeaUAeLi7lUYW2F1SZ9Ekws59PralQhDPz/ZmFIPT2GXarZRQIIFIqFY4NWutN16IMm+eoCetMCZDxXf7HC+/AWGOzuYn85gilx/W2I3svyYF8mxOkOjADcnQIYImyI+l1umtILvaJdMf6lfRqLG1xT/Ditp6Hd8A4Zpzm1JJ7QDwS4qz9IRBJWP2qMPhw8OhMYTE3vPN3DtJ2VccndwRTQ0D8xNdgFDb+ST6vuzhGo4G08O+DOxF/mOW8f20u4MMtNDjZbmYRL1PY1Oi3tjEeRTKnm99Fz70/ldCHsbQfVGruhxpXQXWzMDVXFpKE/j4gpN65RRHonb7aOV15jvt81TWhcUZuST26YCKWpDpGZY/rXdxk7DXK0EE40fHYb8vxb8ib8khVuDQLdjii8D00nXjjwcmrPNUp89jeB3uQfsz6tyOzNJjR07ZBiNJcuZYsNDzUsyul5sYnUBohlR0xeWvkxA16uRB3bj40DPYQBWY8SdjRFbUZn4gByzWqLJabNGXLHiv/WWYibnG0r9kBw8ZbdfD+zhNB7Dk7Ytp2pb3LCI4M4ZF5/0+Eu1FGDGMm8q8qD4VDoaCeugHYlPTO7r7I+ZHd/of3Qwe+39ocG8peQBND5E0UQqJIbojYXHpxYBvyIBHpZ7pIhDKLumlUexBydHw5cZGMggaOAJXTeHPbZl9IMYWwejagHl717Il4D+OBSppoPgTDCw3ttXjX5Ixmr5ZKWwY7KDFQwEpdOlf1GIYSZP5LR4xbOoZuICvw9aNB2yd9iJuTLtZ7giQ30OZ8lcR6bN3MAEHr40CcKPngfpNNR9UineTzyawdTX8lQg/6EaPKA/YQncLUbZuN8366FkDD7CtGjYRBHZTTmawE9oI+SJRfrKeoIL6qunGO0V1a8rUZsdVEPYNmoRpQzAM84F5s8/MkxNvd1yPvjCuygxRZIUg3HMS3iEAASAcDs+q4fr5/7ecR5F2SiUAmLQmdDKfCJsAdv1Q7s0ymiXspSJxC1rlPDKdPHawr+/NCZ8lKa+zbPzNxdjY+svEFox4Edxd+XzU6RHIcm8Y9ITkD2Fb3+DDKI5H93cb+tppxdZuSTkRcEi4rPUjw2/zwi2QHxXaBQ3ScBQQOyDUAu+hctx4nFy33eF162RRP93R1PxBJ9Ba27Z7OewA51G+1UBjBeIWasPUvdDVkQbxHH9O6lUWA4n/8sXcDi99Dkr8D53B28ticmuAb8KihOCxg0hvH1TKkX7PUHhVAZ4ZY39dOIORb5X1EMRXJ4MtGkgCPQBmlEM4dl7XLK1AZJ+9m3L55y4kC0haT8CZEiITm6OxCCnua3wsIDwUPRgvR2picyepn3iZML0+VTNiolR9Q4EdCtcjRiySniFvWuqkf0ZOv9gZ3Jyat7fuGb95Fj3GZ++t7w+Bl8IcUKx9ycGlUtGut2lm29qf4go0WBptHzqG3c/y2KOOfQg/+iRgjzFmb/60uUIfNrlkUTGePN6JmiUIr4nCsB6PEnRtni7CHfVjmAQmhZTtP0qQ2q0aC8JmMNhAF7J4G3F2yPCBN+fVtluopRuWm6EZbvnxHV/m2/H5jOmAgzd6ZbvreaFLi7z4EqRGdHrZH7xyvE0Bw83a6/92cN/SNCoZhGKM0sdwzBBTift80WqlRxJqXewnvnpGI/cwZev9w5tQ1UNBnZlL7o2HjRanpWtDAWDxUjnlpgq5Cc+UoSWDKVI8alSZ3zSiL2QKljRthZM12dOcEuzxdRSNHOzPK3I2KPp8poXNQRLTRVJCsUiDCBXsrwepBihbg3i3WnC5SAeq8fQXa+zyh9QruGoNowAr0FDHHjN/ij+eAaDzk//w5D8tlmovKdUo/5AfFzgtYJr4iHvV/wmt6Q66Dv9mVgKoXqqaeklVUuZrKCAuJuArQztwCqAALBBV2WRthr0Qzfzn43goyV8VAJ5ege5bAjHRBLNsdOEf9HqKz2sfSUM4cy8nqSn2VcBJRr7KTghleg6LSlst1mYec3tOlm7kHt6S8oQzyQOzZA+wUG35mOKbHCbB8oXngLxFg+Et3X8VHvOzmCHynwJyiOIUWO62lyYmT1WS6m9FNZYrL9if1Mf4rC3P1fbWF5KzkIVyA6Krr+NpLDnLHSAbxMjNsRLUOcOVk6qV66pmxZe5fSXHRJnbiP/RGWqL5qcBUgIOJE7uN62rcSL+M6Tk7j8Wuq+1NtjOHbbZoUkr831oWbipTMfTw8ztkcEPthuh+sfilGtGSgza0yQIYI+C1Xj6IK8OOhhSnPz6/1A3X8BGj5jobPTE/ZW7yp+a7g+/tMQZrNwnpeaq+G8hylc+Y0VVkJbSafIzjk9oORTkg6wYclBN9oGodfge5+C5EUhQpPPUeIcQj9OV+kCPi68grbETRGdU908HVkUlBF3wGWQb0UAQBe/C0n7NmF9zqVugsTnmmRH+0NLXS4tNV9AynFWP5/9KCfSDLSFS/YhU+wXDZyKKdOEVB+QH4QyxuCMP3JnApMpROv3M2XOV71D2vC9b92MH6VUYbSoMoswOZ+w7A1YJs2Zgx/pUzzq9kkfGFAxQWHEwQRYp7HfF5nktHL7GGwqbbzWK2/IyfeTM2bDDV5kqukbVTOCE82D+dA/kALGpgfdKURFBhAmd9dLaxjyDZLyTvZZ4AmEn9ZhKVxwOPLsIekm7BCdwyn7QNFsZVlyEiYTSfaFh5QjbEBXhniHVefs0gmn8smPk0zmsRx8VWJEn+o3ktPdLxqWUSfE3ZTBVgBosCmbBR1lqIXxSwEcGzHGLig+raEVzmIJ8bdK4VHAkYt9P5JyW/ubK829wQAdyVFJXj+///YOrXxdWFBoHiA8IJ3cS8aVf+PnbpC76TtjoxXAFds5eJP7nPZLDrs9lJ/O6TOlb+LplOfyDoPXXQZo0Nxscs2hDvxSKtvKleRHPA8DPxC72d6JmdQLI26kbf6C7bCaO7P5uWreJr6QwMfk86/ImT7kjAYIeE3mbPHz2dVcKfgZKpBbzPVWVoAtz7bg0IQuyXWiDgELPl5mOkkLKtM5HwhvKbgr/lu1ScGCTZe658PbSRyNcE7aDKhfaA1PcrcPdekbs0mfVVAyOLXn9Xaes0OnoJyFX6rCj5Nxqg9mJtRbkbbrfFbHym5xnHFNg8ekda4a21IJF7zEc1yjwAgXcmC/7wixHpe83DRepRzOOanlt+lf/UKPcBoiww4j1JittaxJdLoskbEj+mJfoUOxl66zvabLvngSbhyfcURJgn37rs0zhsr0miBubD9ko8Pq6EL5mKc8a35674nZLEyODL5K4X3lkcyPFCrO9q4toUo5dtmMVZfqAOAl2O7kfVCfG2z4I4eNrO9SBCGC2O/wCTfbO4CMaB58fx2SuRlWdS0+QOqgHBy97Nn8FKgRkNuttOZs7I+UBrKoB5cXfwDLUdDqMhvvt9vZZkbgGbZHEcdz/f60aZZErMFjfNDr5/Jw99MneDqKj0KN1EWnTThvTCx36RSxnyOPePP11qLABoxgJ5kbDNyHf+uS5cidwg2KkPNV+clSqTzdDTEb2ICjZz/Lb3saQ/6ucfoWjh+OdYV3dlXxZFZZnXHNJoMgAaL//wpINX76A+7QLipp5T5BLh36uh/SfIAmA3gR7pQVvKi7anZ5KE17DAzX95MxVlxBeupWr732iMhqm41p50ZNkRotrAjV+EScrrGDSm7vb/bXmtAmx/Pi5nhHiKmRdpl1K3vxju6rtWK11LBY1oUo46X3ToTC+iTzkRPqX18Tk3emC0opgpgpOrzWmLtX4sXCi6F9O9MjYv3nbPQ/XK7sMAuJQzwCgGudeJVVlk3DtAr8l02BT9Nx/IHKpqxrwad711+aLHfKm4/kl/GBywSkIkuW6Dnc0HdNcw5CuxHjHe7c/ihwkykDleM7wZWt2WPrgjdFtIx8d98B7Hoou9r3LNq0xKqudoYbjt67jy3aXeI/E6Sufb7mO1iYLIrz3xtEYJXELxein8iAtQq7qqhb9IlSQXapJ0SAt0xu0wJHBhSRLRFs1//2Wzo/ICcFHFIL/E/E6t6dWp23FuotN1BAdNdaE1pL+AaQ4ccShmaGedUYDm0t2MPQTZlE4MZvxBQSCecOV+czMw/3uFoqfW0nKkLK0MXLFFSTrqpRHgmupYIfh5aMo17Qlgy0zk3AcZ9gjNnPBAm9YqSDx+PS5L8oH8uY8q6WJNUw4vwwAVzzrDxTibmlYtXFg6qH5x11dEvJBIH55258NcVzrn+fcb2ECqkIQiy5Xb/62SShKPYosCle7YBuUhXanPE51KTNZaoh4+2oLIoS86KVpdRAeUp1iUmgmpiB3+vZAF71qKnmlaUkgGlhzmGJmpDyN3hhxOtWYeKMRxu977XwlT+mEvKJVfbODUm3pkJlPqd4zOYdRghjFCh+FxGtaK1+jMgIvxCQLTlbkrhoCuvVPh9gtInouBYG/oAc1WgrE+ZdV3PV0JkdOc/sVf7w1GTLVeqA5euWs4+v40nK5JMwwruZfNOMc4JWc1jBT7GeOeOgdRAu6R/KEhst7ByLu908YiZbcolT3XNDPraeKyeTWGZQ1DxWGXdPKWmmvl5NwlMbT8KcZIoJRjtyrpg15yUsXk1sGnTTItAm4JMleZF9oZ/vBPYbKTeG3Rt6zm61EEtoNZje+tTzMFXlxhn64CAYb7VYBfiu7MRewgJBkItjwpaG1WJqvwF7Bjiqd1oqBkQdR0427klgSyCz3v9Lw7iHXbJ9sFpYmjZokTpzc2P941rOLipUlJjbUnrtCugLoAqtiCXWYC/do805A2x3lG16RQrZS16ISk0aZH6II7udvyKi6RzkvbNVzGDj5jK0s4ZEuG61GPKMd8Q2wuQbOpKSjPOTcXu8+KmJBeGtC5JZSF6DmTPhSHf/WR7sX4qfuERGV5nV664ugldjwou7gyQPzE7g5NmqpADGWuucQkQZSRwC3Ew1qY0FJ7jcmh6EmSgTAsy9vSTvv6cyPFGePYnTJe+kqVX2xykDK0Nrs0Rcd05uKiaqYkDKl8NfVP4TGXnens4qrnahEwtCu9OizW1UEg+47gkSLa6nZNswak0F3qi4NpE+ToFVca0KF/Agly/XLJw+YmGxYF7KPLJp+5WoJRe5/7Qor+1DkQrBJWYecyWYEWia+8Ypr3kHgwdV0gYwcuBlN/OPBPlwJTUHre8mQjgFC1msgQlTjodPowddITlt3zQnZYWFa661ptuovfbChzaXBWGlNd1wRHWVWi+518Nbn/rjkj0ful0XVV35diaJc/TYOtt/tkPMs81r7R4KtC8lm4RuSj5HcjXtgfkJCpoWD3FVytN1ezYQO/99udAaXi9GA3COiXbhrSL6IlWwMtlG4IUjjCUQpcfJpEJg7u1bg19pUiNoXMrLyxaDiL/h5FN8jW+0vGsiJXRXjyD0bPBQ+DYODltWccVWmI2+Bz7orRRAvN2H7Xv8xtU/DHzBZKkJODTmjuGZnVfz1RAGe423LHa6LzPbVd7cUepkwefza2hJB7H0ovTQiPooUOrYTnN3drhytLsJtmkNzvNi6uxaOF7FYWJq7KgWoPUD4BLqdwndye1RwiMHk+soqNZ1jKgLlB5EQ+yrcGMSX0jqXr1YNMITiSEN0YNHG3vk2C01mo1zdhk0bhjBno9q6YmIXw8ynG1UhzX+oPqwQtAHFtRr4kzTF22Jt9n3FPGb2Z3NrWKMXgQb4QN7XIiM3JSxndIpHSHW5ldSSDUqc5um+XdpvyuLUs5oqyWuztpNtXq6D9IHMyJ6VKAcPJCYu8b6SFrYe2eryBbm3bMODBgF3ILPwPtzSyA3TN2fcDBgkU6/I8esS2iC707rZAyoP+a3iw0zfqLvtrgIxo6sHPeAmg0OEQocJB7sPr4M6mRxJEvna4E1BG8kFZed5DiPDLPW8B+/9Jo4nJrKx7RQx7akLJGR4GAIliPLY4GXr1xsEhlJP7/5MU9f7TXhgon6IbU05i6vj/QgYKxybsn2jZ9Yc91N00twrjg+gCa6mOI4TFoYx0oWeXInk8kzSOgxv6I+Sc+vLuB7D3LVtDouqPKeSNi7w01EakQE5YKSBDLgKpD0ZKECf/8WUGiQzn4q50p07YrWAYol+XNO/lnqrvOl//obQ/zZ2jIYTt6PtwDFhpJU6u7ISWc2rNQ4et8x2GV/lENE4nDiBVgeQ3aN8P87gQ4nbhZJ0XpwB5Hj5vPWlRlnHIMy0Tqxe9a9vEwYUm0JH936H4QimNMZCx9lMw0VVFIHKtTqoyryH8waTRFlhgbtSzL97HwUP+nXtoZjh8A5/84pEeRCp5suCaC1i7+4b3TWmBVunfa7K5uUCgeiEY7s/GZRujKU9aRfRwSm8VrxFD6+Q0lOKhgVffNVBKcCUspjX2pjNmkSgUjvKRo8zbS/6uc0o8yb5vNwkyNzs0gCWsSl3OtrHQTiBT4oTzi3PREu/KAUDwpQrWr3I8c3Ljrw25s/tZR7SBsco0LDyKgnmattesxsVk3jzA3gnIqqV/Rbba1xuFCkfVjOmVtg8U1G2JY1y8ss066tIc9sh7pJ7umse8v8NdL9jxVOIhTYL037o8YR4bNWEJaH4wFCNqnMh3t9qOnCvvVUgRrNLQgk6ZzUegmP3dwIhBP9VVpwpxa7Y63yGu+p+FxGZho0ufuhIrBRnJOv3wBDCthKFEVElhtx6sYBndXmMm6RppinLKAj4FhMykeeL7GMrTmj54DwyuXt0DAfFSNdtRZy+JQwtDFAH7jdf5Ht+h1u63NNYQ/fWgZdckdOsvAprTqLnm3tk2DXmavpckyWr07ZJ2PSKX+h9BbFvlENd4P1ircUSIy2DGg7dyvrXOcGDBm3aJV6SRozyWqp7ctCXlW3xVbyhNyLnyFNOnxhrMee5O1BYtpd3sDBaBvGIVo92ADrKhZ5rNhB0vWqJFqpCNT5Dd3R/hu6MJHa9fx1LKsKcerqyQv1jh7j1LyYEqt7EzTKjJ6V4bz6lBExegug8zXUobjQ+XTLB56Szbm0UMym3FJZOhquifUTGL9F+jejfRV5f8cjCBG+CDjIId2Skt/iIX1ANBDqfczIpV+SPwGBgJx1magtIwGjF4nMMGpAjobH8s9+jXC4F6MKtW0txHrnK/9y5xyB48ZNyoQ6KjzXu1iVWrW6guOcGfafk66RFb3phDbwAs09H4rQxjSdtQpXzlyCrCAcnT46Ndp/4NeUep/KZFcaHPdxZuQlJUMgwtQBDUbjzoBjn8huNBkEVWvXO5tPPjh/gWgJi+LkTRn0l+XC9OsAeb9QNMqg6c+8I3hXvVskwAhvfY35yJxUr9vGQHHoHCc+UP8lK+maAePvcsbNmnxjrRScLmHA/RS5AwehDa0VSb6dCNdw2z2xuefpVWzGgiWR4IK3i3FF7A7z/YB7vl0spUHxeFJT0+0v3UEoRyxHJyCee04dQ7yT7fHp+aDaZHBCFwCm0TcWUxBNnefjg9y6tyzep0ispQwyU/qwVwqWWTJmfy7RJqe0advZsU6IqXwGIitMQBCCjL3Ny8IphDck98pM/ooLIjjM4yGu/qrkSbzAQfXf3GQZP16rH9DZ2kbat6xfw/wnrr+bhZb27xNyNZUojKH3ZMOAwvuj2SAry8NymAyuimQJ9Xm14xq0/f/ki0rfK2gyD6AOfVLN5i/DvBCGxbVH1AcYoKY5BwHVkcFO3iAL4kdW+sxhREyTmRMngrT+7FWGUCKVTqyrLCsBPs0ntMziaSMKcSyXBf7Y7Zn6+28vkUPTFp8Y9dg9/NKx2hNyYTetgqR79jp+5v288L42G/wvCMZjgXtowNFeTZai6qE7irhrfObOYvufWPl0dEJvuTXBCFZbv8Qh3IYyzO854m8kynXFTTsRAKrOzs1T/ISbcUztdT6wytBPj5V2t+MXJ0eQmwJRGvxIKScXoD2d8YCYZ1SyLhOusE1Cb7LRv9bHz74VXwL9RnaK9SOMHk+mstmK/T4mu6o2Xmxv3sr9YI5s3L2BCQORGKcVJ5hqSeroC7GaHEl+qP8UCR5cTK+Z+bT5EvODgfKYg4P8dGfOJKsViDsO2YyHFiu7zvKwuVnX1cr5g7jz4reW7Ze81JlZkWxCuJZbNCowSIt/X0UMWs8kcfaSPNRS3EjXu+ggE5T9d8I8spFVj2oKjKceaxRO4wD0j9jjzNGWAMj36QnaV8goIvJtqTok/I1q0TUqanzx+7Zk/3RLCtqFFitQHWBiMwsstyXW2tKVXDVwA4oFDcm6z2FJUu8j1i6wkzqqveoT6gmUrsgZkR3q93rH8DMPB20ZQHXcp2kTEsf63aue+zXhYG+692g5awxS2vHQBZ2rMApIp8mkyA3ZkOR4ljzqd0szH/bw/wAOXWG7blHFuCvMretaIdPx0/4ZkScN2V5CUvd1a6BaiR5EK4sf8TDQmGUygHOIjssHnmRqNoCGMCiT3EjNJ/JPeO1EX1/Cu6eQM8mK2jPZkqV4K8D+IR/2EGUgI3HhfqacVG4pw7t8rtGCGomPEnfy/4B8caKxZf4th5bS4flWvhj/Y87+U415PDGiG5cBwfanaj7C6b1ahvonJpNW1QDnYv/SNsg9Mj3usHPBm9vgiKSxOlEjNsluPaMNcU1e0mLvdGRUL9p0eylOzxuqld4ALmW+vDCjNUOIcN6S+7Z1GIcXsrBk1cl7JLqBMMBlxDJuDRAHXeVuz3EZbMQEZyTBaxhpIEylLxhA3f7CNeiRXc4YIiQl3Bvz0v1pRQ9cAeFD0kEC54V1klr7dbqXxEA9G+tbHDBXvHaRAdQVRyjwLvkxpnZyvXh0D+mGEERsnsuEAxJscNW4uD7xvZE5fgIwbP0jY5maCkzANavPWK2GC67me+Z91TYBqpr3tJ8z5eZ+sPMybI+X/wbFIXW2hrq1uEBn+y1z8jdkjHRAGZ0b0uyjtH4FrKhYCiXzLn+5AGZABntR+H0mTj6EpXPmYUdak1Uj4Y1CqcamZQ4uR2r3atBL41xS0rGJWjX09Q2zKJtdd0ypcj4ALFLkXJHc1RydRogro8cuv1t/0obMT2x5ek8EJbbqff2whrHQL8WL2vqZeF74JijS7YrMg180O4RD9JddEo/VZijwdKTei5mIBC9YnNwyOl4KHt5I1rbaL79gc0gUWRArJABr47CHO4sYh61VTRryD4IxFVsYKl8hDONmPev3Lg1mTJmh34HDmAnOOcjFf4XuLi4CqwzFGrwfIOpg3eMWNhv5RMyKjAO8USd/7SGhzUbQ/lVauZoKoTeb/tMGH8bJ4QLe9lS1Q79JIyKzI9hMSavzb39GAKMbnHNbN9Vr6pPb5rjnUTAacfWSJ1LRfD5YbsDNOlatKfaLLZpWueB7O9GXBx+7NLHPdmq2uoWkKJhQBH/hk+HDvLqzGUtzYaZeXlegtzZ3icBH1FrSKho5Hv0XbEKlSqodVBDMhjfgQeF06Q4QY4VL5Lr7kLiJxauT5kL9rd8jOWjiAVaA/jAA3ZYTLYjKbl6uJvQKoj1wk9AlvUVdDRibpZE5vTFESZfzFbvytKcNX/MS6Owp5LRaBrvqNm3S+8tPqkyBTNyCuxhppdcJmw+HXiDFWcJeRTonSvMw31Yq0rTZIFqxNIjVCH2TBQj85OUPTPV7NlymcndgS1qwTC7eXWwFD8WrBpnA4UpX+itT/9ZSjavehXGp3j+rbSMkuQ6Ev1YCneVzCHXtBLkhEF7ye4Jg7Wp0z7Zn1ZLgyxPL3Zx0jn10g3rrKL3BaHkong7+Z4CDAdPq2sCtY61SLXS1Vjr9vjrVja4Q1txknXqm+CWyducw9+LNIsRKXi1h8AitSyZTl0YS4CwgAX0Yet905gU9sIVoY11pc9KU3eJeJtIm1cchWtfUDlTo+9OGSJn8eKl9I7zB5PPh0x0i4XDcB+oz5vZTprAiN3iAI1i0eQxxYf39V9uIlsW/y3grcnOK4bZJG/YSLSFQquR+30kNUx1scM//TdvqzRfAAOSPIJjFx+y0wh8YAtHHEpG8bSsOz78c1UzfKsP5AAIAjkPby9o87JT4h0NpY7vv7no30t80V9VKSRQD2KBgNEWK0JRcXF5lN1IFOjI+jUEm/buWriTKgsVRdze/t3t9oEOfx2/WsRtIWZJBwQUbOrGGGyY+UQWcRGx8qjce21zp9ed/i01FZ2WZhC1RcsKQT8p1irVORdMaaloWogV0DomV02R6Ao88YHgKqOekSiKLt5TR+IMcctlvFS8TEbcIjEOO/1Dzhh2jjYzCaJ8pCS8z8smebHGXuSujpIjGOY1fyT0oWacNm9brD1MVg4eInU/I1m4aDLX7vrrccgH7rqwabQl/GgtFZZsLlBcgSdIOjN2BBWSNGgqtc9p4hLJd3GI/IfdgmnC3qWTvVX+gtsSN1fqn59yXjJD4Ii+1q+TdWWfx99pzwqaw+db/rmUV8zxu5OtxJ7VWZzgrZjEWzYaLtxxB2bWbB7/FlWlAxqpsDMhWjzEkwxqR3E6huoOB/HQFhmivKU+oB2yOIM0DbLaU90h1tkurQje0aw/EkopYpCSMxg9VazrusuccHseGW/gp4oQ3wDM5Y/zQxfRc7xetbrSzF65dzJgcFfR4s2aq9CvmZVlkNWQpoe99FN+HTGLK7+t49V8NvGyMTd8KszbG4eXlaHznpDj3Ls5SC79akFzucs/ZmRSHGVaYLsx9eZ1BwnkauA9pc9X5TpwulU+IIa8gAImImpGP9K/1SjKSfOqbQBzGcS5c3YMGa63g6N2PpQx8/ylP4pyh6uG9lpw2MVhqudGyn4S8UsRsunHjoaqoQol19MgBtLWW9rvU+Hy1chrXkeYCOrXpte0pL/ODgYD9CQ7tqjxzFicZFZekoYRultTnQ3shxLxSeKxFKddORKMj8xmlRtLg68PUobGzzlM6kExl79VgNCEZggAdzhfbdZuuFoBiLvl+nITFqV30RgAXa9tcvwY952pv/25Fh8GSXgmBNKv+U6zhjmPMJirzhdQDGRrqXW5WjUsoiMrugiBrsfN5gYazRcyqdo+2U4G/YIXl5gWCdn8pqQn8db9aoyMw29gQ0xUIeFNj25XHZ5Hs8vvvEcojvAaOZLPxT1ixGlOfbWv7MiH/YlBdo7c666LFuvA3YGBMV5rp3+Cz5ZDv09ynIpGfn8foXuN0k5id+dnu2dPLYq+7JdxFmIaLTiIOXJQcaBVR2RXHnqnkvq0Sl4+N3imc+GGWZkG7urtYtDCgtVJJ1kyGoecvVRzNADV8JKygSKDRY+M9+edk6uq8zgy6p7cVqvdLAm3FJRaVK1TJyLpWOJCwjKTLRcdT2L/HgNC8iFkgzWR4BRehBeRWVhqm8zDm56q2lSuOjfKd6m+jZViG2cLMZL0b2Ik0g1I/JlJvnIeob74tE2+ZwKamKPYyTKRW2aTHuRmJCdK1ESCUAKkMbRLa+uGjM4TfIRDJwC3c/cSiLnojTWj5LWw==\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design →"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"db2RfIwt/EgN7Qw7W7gyu50pUYtgaOPhhUk8C6UJ85BEFwjrBoRLCVYHwFKlrlvbd5LLU3crSpUtkMwezu8tC7OELMqMgl4f77cvqql9yCqIwP6kC/ULuZhqeeBcgqwGa+QRh1QuTT5atClVUk3/pBNRXF+tCIKqQtKRNXb/R2HRcHn+MkmmITmLw/csoSsA0bBdsbUuf6F9FtUM8cbVWOQMbo6lL6nevnqWOUmKXqQeuwWCaNH+mbmgu2gGruIZAiO7lS1NmcMx1HblJj5rwhvxfbAnWqUL8bc3tPYQBT2Jwtaz6GTkGzXioY1yK19rjyXZQcXI3+gomGjZvCSemmSXjekS4II3Cp1ZFw+Ub2QTfk5ih94R5nWUxIdC5lbXyVQGkEVlhh4mfulIkwuSvKAAhzfuFeP0z9GM8ghItDrRj0QCERiukneezpU6gh3R25N4pD6aySB3LKRdMMWTIermlT96vRnAGl7zREFzeoAvxZ/Cukt1zj1hClPbz0ZIBuz0vHzF+Uaonbr6PpO9ZNSvUmDl8VBiqDC010js6ywQuRUABc8KmUVGuKXiPoirKTNlk8gdl5mis5hkPfAIBlKYgLcdMa1Mpypi9VAeVgsGxGCKCmdrxL1FwTVwz+mefenlsta1hSpybw5u8yRkdUFyOI2Aeahim5oXNDeIJjxL3xV7CGxvlq/2/ZNte7KZAfSWg8CtjJumFx+G3EaQlBLL9Py+ereiwEy+9cWZX5AhsaPrICf+gCxaODregS8wPK7CUpvtjG6hcCN57fmrVSzLEkDJm6jCADghImUrf8U6khZeILQub4NGUjUa7/KWkNUkAVIZ0ibZUMs4TVMD5un9AFNcqqnP8zoP1Li8RebzbRtTgXoa/BQogHCDAf7jFBzz/YFxphIEtWhxnwlJEfntUkvU/JYqwS+5mEg8TpRtsWZ+IaHsLtaLSwwZ71K+rAwOKGSoDcbPvUzR119j3vaVhljpM0GTLfPt07kJqHEN7Sp2Q+8PgG8MecrdTVAYYNFKYQAYuU10WZ9KhzzOKGu9Ziyd7pexl3ZDsDFnm9jS6YYB/m2tT+JybP3MTPNv2GTNwcf0A0hpc0iduC9z2JNeytRqxmLQlX2myRHaEYt8Z+Uen8GJPhZ6dosxCa2f64dv7SEYJrW2+d+AfYX2mckOP3PbQnLqJaEnKUMtkgDLLrkz3keWc2MsgjgvEE47zpWliDu1nshlBOlH6fVd0pMbyd6Ncv/h7NBAzPd0uJ6HIWBhX5vyxSBvGP4nM86xoYyfsADF68NEYhGBCE5pCDmc3rH4QrVVeWYkUHXNGIdYDsxkvt4i8jt/D3Y2cK5KMmTRtnmdU2zp0N5LUTKOHPvuFMPbaEGA8lSEEol+F24RkFFVywB8k1k40pCrmKGHN748U4iJEHXKLDBgFBRngMd2HBfK9W9Q9sPAAV8kd3o0h5pHqdevHFdA9a+UDzeALwwtz08NRx5Rfv9ZP6Nym6xJdQ6qmz64meaXSKtyydUBJYrWrCVmd9KV5MdACbLL7Y2zGGYUvgunLQkU5Ixgd2JupAt/TSfXrp2fkCaeIwlT+4Yx8LSDuNx/q6/CiUbniZgWbZOkRVhVFYS929EI5kYif1/cz4YKwN9kLTsDJQFryJQl6OEuqEfNscxeQtR93hVIqEThhuORd/tsJwXoCOh21rZyB+fi1MVqJ0G/MOSpuB0smIOk8DY5o9gurCO8KGDPNjp+lT+zpz12j20dzCtrCsdohBDL48dFgAzPxkYxI3b9RejL/ilrQ7pdPYOLstUrIlYK2q5bxOadd2SsT1935UKDUlIWr5zV63T88N5qEdsJ0EdtM/Ke/1SwvKduyasy+fbNNa17cMtRIF4t58XgriqKT1Te1ljUOb1Lq/yHGL0zYn418Kyrob5sAgTMrFGgr9NaNj7ESlhhQqJntiwBoGCgGZI432Q7Za4vbzyoFodKTK6CgC2gZWofK2zFb1QWSc5vXdQx5Xk0xvt5eQun5mWOQ7wEgljHYIVfo+NSQhnS2NiR/U3ae9Cwmi/NdG3gJpvK3fLydaKAzED/MF+nvG4xcCDxXRE7suzSObhiKrfYJhbahW8WWkXLHf9/+m0Be+nVA+kgOzq63UBVleN1c5Et02zecEnc8jA6vAsshyD6+O3WVbOxxqAo4l8P5udkH1EIFfPr1iXgEWHif686shj1MiHBcqiq5+vhn08A6VHbZ3P2B1O9T46jwvWvSQabZ7YvQ030gMmahCcgdkWLkYtn1ZPBwqpxs0J02lBG1An1kj+kUyOxu3+41yFc4UhTJ+EuYqY973iROZDN56R7hWefrxX0G/0WpkHpQUtstq0+YPNee3stS+8y9pgHqsZNdfCCJYib1IPrq0i5LsK114bWmLBQEZ1tVPHE8vZzratOTfyJg9TX3CJi7Afk2YHoHMjCzaCyOYnH3ym2vaRdzOwC2S2I06upCM+HQAqmaRMxqF/BLTeUsL+HkT52Bxq3UnZGQ1SWUDdya6IA0u2p5TOu5BFs4/A4sc7AG/Q1BWEIXbmskVWlgk6XJpHU9T+jO3PxPsTWvQGRtS8O2X7dBMCbyY99bUog+C5ljzSbPIBuIBqZKZJy/98dw4Lq5ZCBisGQi7ho4YX9svuKaCOs+Rmh2wwYYq6Fd+xH63B8DY3VsJr7Vw0qn+qUyvIzUU/S80dplg02jdY4rNM3TqFUzy1NPK2jYIy8ZIiddmZR/TlQW7KYszpa2PMPVxFrUbW3wiqCh1ffhM6JfRr22yW0L05uj871nZX2zUEkccgDC4KXN9AQ0V/WKP1of1ulo92ldHbUk1jhkN3to852uH9JkpTHniMeR/UVnaZjpalvIcexXENYYhuUVV4/gpMMAH+CYQvINqnTMcONVNR6Hrlob+ojMBK6poeGo/r5q6MnDnOaVfeFPKspRT2wA5h//oDIr3kWg+kfv9/nv+9ExN0AF5ohBI4xRrUEoQ1r/GoSn5qLYXCb+Mta3YKsmWPsMDaQrKRGz2YPA+XmByUiZW3wgpz+o7lJa4aWqenTpabgHFpzlMwlZi9/sCGPiAEKelI87jBDgMB6bSFOAhatWKHdRQecd2MNwS41st8l7WT9GhOK5LNBvqxLBeUd+5PYFIS7wYXa/j//5PFdt8/OjBx/30b4aI5u1GC1y6RKwac4HUXLuiAPW3bsD10/PHKFQ1Vo+wMNyA5yy1P6geHZSxphHGlGgXOQKhXou8rczI5PFetekzJBrVPRIZwM0kEMDz0xsbNDwmAIlwMXvyszkukS7qdJ8dRX36hWonMGbJ4xJ9sqCzeQoznytO/TkmUa0MRtCM+Zul7X+Y0i6In3qsntz39648SSjowzS+0gaXzV1ZJVwoboSefVVNp/rODgByzJTTv4+MtV6rJUrvolFH9ORQPhckIDUNFE9wiWMSENYEs67vTmFBcXIwdVRaPf2A68IhG4IPNqZb5FbkGlKdzkltOSJ/dSZOYhOeEA68dCNLmsAvu7xzOAR8reMJM5zUStVWGBuMzDkjYBm4hnANXZ6BXfHDXNMC+YZ+dFvRZXcRwra1IEcIP2DanKa/NVGn7R8iy3Qv7qHJhPZtdU+rq95KEGkZBLWfrZKwVlMtZ37yM3XF40OxGi8moNJ30dovwNdDLSVtI0ekW5i1yGXZD9bXutYm8TZJBoXo57zdVcZiRQFaMYVF2jV+HWJEzx+FB/tUwhZp0jBSiAk2pr7HGbNmV3sxPh+VIVP+Nxj3ztqbHXY0/wVcWChp3OYWuGfKhaqymkFTk3ULWCyugix1BiGRxp4+V0HVAgI14X45u4F3HSGeSzAkf+F/vuQn1NDDU4F6/qc3fgpsEUYjDXvftZIttL6XSmugIgWXmo6RSIuhXs5pOisBDJ3fc1jNs87o/6TVrDY3wlrMiIjyIt7fec3b17NtutdIJVSsjd0/mfrbMzzvnMyKK6Q34lxoLWwKIejYikfPzwSX96jDfx5Uz8WTq9nBV4TUQpb6V6Dd1xnmDnDT5qwjIpZLHwivnaVg7v/2EisLe4lx0622aTAVRFjvcI8AqqWPiSVImj80UIkcOEdJGkVvjcoC4Qb/lzMZ84SCBBeCilYy8KpXJsK9Cu1tsb3dGBR2GZ93T0Alomumjth8xMZJF8CRBsQVPEuZVwzQjw3dvleELz2cT2PtQGLA1+t+QiElEBRYrW7Bp+jOuNu0blzJbiFpj3ODT7UrXIal92iWflN0N5AakzouOuQaYoFEtJTP3Lc2HDLfWJkPI4NrVMFxD3OV08Mbo3jPNMumy3VmjcNOGtfWElY7/KrlHN6yfB43NBaYMqbArIOdnmkANMgkCxoV2xbCwz2xC9ksKx+EtKXHmBp4gs2vxlAzBIrsOV5wjVEIIwavrTvlBmnr7vENLak7usnBLPSIXYrkLOYlpY+7J1Qr0Uc2N6E+mtEtqyb/nDAyyWluOHvOPN4ZfsPByhye8RD3MjoZhPsfRYY8mvZFpDR+cBXwkA3MDDTe1ouSfG4xYvg1b0RTYZ2UEIZtBWXSWGysSRQLGbBOU6fb9nl5WkVKjPCfbrckOd7nW05FHKCCeS3cUh8ILs4osr594ti/yb2gOm5wFVeFltpvu/Ojp7Fa6jAPSDIehV2aRdSI92YQEaagosCrVRDg9GCznZ4c7W1n93oASyIHOyD8n3XF2oAXk84FOVbQvklpKPt2ASRmlY/p8Gb41rxC/ZEDLxpx3cZHOUzr1Es7c2jG2OKTFFauAebhflGbZOSBryUDamjS5Twu/NvF077a0vJ5pyZBorBWYS5Rb1AjXuCE0JCa4TQHy542tlll35tB1eW9FC6Px4Qernh0Kkq+hJ3m3uWe867VaBcFfsWAaUFjtF2QEpFuZIswPveQIeYCcVL4CCZZqctMoeOXAgdM5KLuGV9Xp0r/aVt/T5bFwBMIjPVFtSr1kT7nvip44jV0/SmqmgWtVupfPqyNJqA7EVqpjegNWcJ/R2zCg7/npXSsc4cGQU47cVz9sq8qQAQmZwRSg4Ow0vLfQNzQwNpjEYu1F0ZwQFloW9O56dvdg5EWHsJL0FrvD2C7rgeX609NWSNSD94VJ5jxZrIw4IFmjARpp5oaCrxv0bHCygTtm1YemZij6KqyAKW6o1he1TGACJB0OMFVRFoDdxaYFKDIYCazQJY66nf/MhrBuO4HsgceaSqlO5Q/P1XbcUbzx4wc+RoE+S6qw3DgzBiUBzoMwgxjuT/k3lyOTShqvdwi9AgXa/IS5sfOC9LXQf3N5bvhNOE+O4Qd4M25YKz2gXm9h4jnlMwZkVFujrnQO4f36AMyY3aXe7/ulaY54RD65G7TiXwsMH0YQ2wj+gaGgEzzLjXCkyQt90ZuCqC8E9c+XqXsXikAHYWDM4Ga4YkcXsKkPfre2IEHMpoI3thgHI3PBIxxCQ/UmoMJids/K9+jdfCfzdNatyv/5xpdHqX49b506xEmFMSROas89a8/96r1KZv7sbvSZmmnrfDcmbpwmzZV89O2eLXAtdYgqp0jHlaYVUat80gm7Av9CNdHg+v/ondgsdTiFWXig9mSUGtGenPMs4aG892EC72CvbCyHW8ID07iQHWruRr5iB+l93qw2YHi3i1qIhKIfHIyh2WpHHkFIFpU6lZ8lz7xAuTK0y00bsjq7nJ+iT3hvcIQRo0PpLuF+SeUnrU/d5mGQRm2bXvg3qlumacoRn5LWow0PE8gWFOM0hLJGBE4nPgLL7KFtTKq5HSRy1FFLGtANFNBDy17Ct4lUrLi9CFIxDJufuPDef28FgZjG5Q0wEqejLYiHdTvoBbvxBB5/P6xnpTnidLiyQMRlLRbYUM+/zDnO+gMb9/2ysEdfRbJ8l1Np9Hh8uyxd7OWOqnUpTW1vbP1gybmbvApE/jiHJcvUC+EdwGxc+abBxXInQMWg5z1C5CoDSi3qRONpBSX/Cwloi4xeOtEjENpVcbEqJ+5txEsT5k41x7X+IOXgxN2S4p827xvWJWWmEdE5K1J/LMg6VdAUcoM4omw+jhsefzbbXseAoEU8goxTMMz0r3VXiEvnYQr9uAgGqDozv1Jdow+MAy6+9XKm/fqLEfHEjpLLXkpNJAuaGim5B2pEIFtQ7mVlnZcdgPbkzj1/1sicqpajorUMPUuP6i81rHY/+HSoTfYAleuZNearLXyAJ7x1c6Yhh0vD+TF4PEw+cxlnFRwi6QuxA4LPv4EbuQYM5eN7WeGGulUvQNiO6YLvYHigKpPQ7FiVH0AHbZXR7jzOPgsSnuDsCJJ9wy+dF7DPdg6g6Id6H93qXWTXyzIhGIp1NoMKXoDixBmdwa7iU+etDqVQtkwyE9e/8MIN5uBIvFcZ70SpV7QWDteqz7Wgs7aAv2om62WPKctFPo0FWizUz6kjddbGD7KRzicxjAc0G/2H1tfbwIBvtUXNmYb6Lz9Gg1fYjCrgiBlkDYKNFlysjn4jrtuMlXnbDg1Rs2b65p8AbeDSqDBGsMjd/UXm3KfjKZJaXtX9uzz7CPRC+W662mMgC6AUGPKZL/dtwTUhR1xAkzkSapUNKupE/f9IcPKWLMQ7ZHOlJxvzXvTF8Jjqt9ecB7cDuO4h4vs8glGg9QUk6MIx5SRMPKn6FKEa9zgDpq+2+iUla1pn/MQcHcfR9tYB2rQes2ahuLN3vrS8cRaYVG49D8DLBdn7b0LJCrbMYhMJtIEUrJOVVhXUjRCzjvZuWj7eh3tvfHL/ixL4BA+rDLG2w0cu98bvXnulZCEgou1iQPBx6YR+bLmS6gYzcENwjaskixqqMHzPh/QljCOhcT3riJubfd1QVunyi2705MC7Q4M4g6Lt8SxCCrkj2iuxD/jtsy3t/PWlyB+sVTmOjEUp1Z4buqx8NlhjRgNXM37oGhCFEglPbsqs6vXX87zm60oj4CSNRX2xrBoDzVagwUJwKHaqjr6/N4CS5BNK61dty/u1sabimbO7am/+yuKR+r5TSO/l4MyVll9YEhuGlcMSvSV37sJZ2pgIyQviWpcD/wFRKqQeENJIyS8MHL8V24Z8L/QEAJSjFTk1IgxKKC2IexSl3BBE4d8Cl+sHU86uNHfXy3pffN9IHflt4uEMC2yRJzQT1Shc5ofm7ir5+3UOtq+ipeHIl+p16MS7tvX4/P4hOsexDLvUzDaC6/B4g7MXv7BJn+i2Y8FfqcJpQ//pKMqDqECDb9SCwT9o/qfOm9TCdnsZwEPnNRXZiL8JCB/KJy5DgnLr+pxtPeFH0N5BoknXATlAVcgOVQMA+AifpkvmLeylv5bgmwxw8Ji7thYCHujBC/UOMpvySeGl6lyg4IY2gMkHtUmuA6P2rhoNR8d5gL32li8h5eUI3Q/7cJkoprP+B4wIc+ncIQTKDB9WOXhGV988b9+YOe5ZeZChn1yKdvm8JtZ+8t2CkLID6Fl7GWAxbG8K+8mwTq1x/tYdZGflgiUXzq5JWQ1+Dca6YBAInEHTJsO1SJ9B0dbHJZRXhHhXySggDmoDmP5cTwH5lfqTbdFyrv+PpJBpACEXZ+gCJU1VMPip83AkTmQWe5ltiCM8heMUOEZaZci/XIGXH79J4b1Y+H/qSG9+mt9mt53av5TSSVeY/GnYy/eMcQHCr6dnDts6488QIzcn2baeMZslKURz43k+8wHGgCBlDw0v8UOusS5aSpSHf7al9boGAouKiVzaLwKvMes1i4+fyKpEAHJ0bM/zqYKqKzPZE1AHoFcbwNPbtbj+UZ6t3eEMDpe/7KMI8WkzoP6v3HsLLBFcepVwY5WRcXaChpD14gn9TGsEZ0bmEflje30As8DpaPXBKSaIJRvDefuBYObm3LyE0xZIHLISqUeGb9w8q+U+B9vp2u7a/f4pVnHiDh62BgwFlbwJBjWsU0cZTTQv5z1ehuJKquZpkwf96VGm0u7+qLHr/UiRNweADpINFgvZEcLRqsfbS5P5HY8MWHkQlOplizpopgg2de4atKtfXC6q3gsFVWJv4wnokxa2urlgleUuxNE/kOjZhTBlIwE+PGQBwMz85kRDLVYZCCAi3PoO/0WEb4Wx3fH+Asune6YrMmGz4Hhk3wV1HTzE2+1j6xiDIszjmD0Uq95TVAqQWD6sBz+Sww4CJ2F8N5dhodvrPsfpmoEZdl/lDMtuXFXl2q1XdJfv1C3PltE80IVwbu9opvocY9C95dXRSMGPl96PrL0+eRpcxy0mlCBLD+fujSNbS2ME8uMId+2I494t4eProErDzvKMKRXQia4SxTp7uJcMCOlHkGEYDw7/osgX7l46ht0zQaw2TRKRknxz1Tl2dod8M4gNAWCePnuSLTt77AZfq9vyyzqY7+jDOmy9gVCazeaUAeLi7lUYW2F1SZ9Ekws59PralQhDPz/ZmFIPT2GXarZRQIIFIqFY4NWutN16IMm+eoCetMCZDxXf7HC+/AWGOzuYn85gilx/W2I3svyYF8mxOkOjADcnQIYImyI+l1umtILvaJdMf6lfRqLG1xT/Ditp6Hd8A4Zpzm1JJ7QDwS4qz9IRBJWP2qMPhw8OhMYTE3vPN3DtJ2VccndwRTQ0D8xNdgFDb+ST6vuzhGo4G08O+DOxF/mOW8f20u4MMtNDjZbmYRL1PY1Oi3tjEeRTKnm99Fz70/ldCHsbQfVGruhxpXQXWzMDVXFpKE/j4gpN65RRHonb7aOV15jvt81TWhcUZuST26YCKWpDpGZY/rXdxk7DXK0EE40fHYb8vxb8ib8khVuDQLdjii8D00nXjjwcmrPNUp89jeB3uQfsz6tyOzNJjR07ZBiNJcuZYsNDzUsyul5sYnUBohlR0xeWvkxA16uRB3bj40DPYQBWY8SdjRFbUZn4gByzWqLJabNGXLHiv/WWYibnG0r9kBw8ZbdfD+zhNB7Dk7Ytp2pb3LCI4M4ZF5/0+Eu1FGDGMm8q8qD4VDoaCeugHYlPTO7r7I+ZHd/of3Qwe+39ocG8peQBND5E0UQqJIbojYXHpxYBvyIBHpZ7pIhDKLumlUexBydHw5cZGMggaOAJXTeHPbZl9IMYWwejagHl717Il4D+OBSppoPgTDCw3ttXjX5Ixmr5ZKWwY7KDFQwEpdOlf1GIYSZP5LR4xbOoZuICvw9aNB2yd9iJuTLtZ7giQ30OZ8lcR6bN3MAEHr40CcKPngfpNNR9UineTzyawdTX8lQg/6EaPKA/YQncLUbZuN8366FkDD7CtGjYRBHZTTmawE9oI+SJRfrKeoIL6qunGO0V1a8rUZsdVEPYNmoRpQzAM84F5s8/MkxNvd1yPvjCuygxRZIUg3HMS3iEAASAcDs+q4fr5/7ecR5F2SiUAmLQmdDKfCJsAdv1Q7s0ymiXspSJxC1rlPDKdPHawr+/NCZ8lKa+zbPzNxdjY+svEFox4Edxd+XzU6RHIcm8Y9ITkD2Fb3+DDKI5H93cb+tppxdZuSTkRcEi4rPUjw2/zwi2QHxXaBQ3ScBQQOyDUAu+hctx4nFy33eF162RRP93R1PxBJ9Ba27Z7OewA51G+1UBjBeIWasPUvdDVkQbxHH9O6lUWA4n/8sXcDi99Dkr8D53B28ticmuAb8KihOCxg0hvH1TKkX7PUHhVAZ4ZY39dOIORb5X1EMRXJ4MtGkgCPQBmlEM4dl7XLK1AZJ+9m3L55y4kC0haT8CZEiITm6OxCCnua3wsIDwUPRgvR2picyepn3iZML0+VTNiolR9Q4EdCtcjRiySniFvWuqkf0ZOv9gZ3Jyat7fuGb95Fj3GZ++t7w+Bl8IcUKx9ycGlUtGut2lm29qf4go0WBptHzqG3c/y2KOOfQg/+iRgjzFmb/60uUIfNrlkUTGePN6JmiUIr4nCsB6PEnRtni7CHfVjmAQmhZTtP0qQ2q0aC8JmMNhAF7J4G3F2yPCBN+fVtluopRuWm6EZbvnxHV/m2/H5jOmAgzd6ZbvreaFLi7z4EqRGdHrZH7xyvE0Bw83a6/92cN/SNCoZhGKM0sdwzBBTift80WqlRxJqXewnvnpGI/cwZev9w5tQ1UNBnZlL7o2HjRanpWtDAWDxUjnlpgq5Cc+UoSWDKVI8alSZ3zSiL2QKljRthZM12dOcEuzxdRSNHOzPK3I2KPp8poXNQRLTRVJCsUiDCBXsrwepBihbg3i3WnC5SAeq8fQXa+zyh9QruGoNowAr0FDHHjN/ij+eAaDzk//w5D8tlmovKdUo/5AfFzgtYJr4iHvV/wmt6Q66Dv9mVgKoXqqaeklVUuZrKCAuJuArQztwCqAALBBV2WRthr0Qzfzn43goyV8VAJ5ege5bAjHRBLNsdOEf9HqKz2sfSUM4cy8nqSn2VcBJRr7KTghleg6LSlst1mYec3tOlm7kHt6S8oQzyQOzZA+wUG35mOKbHCbB8oXngLxFg+Et3X8VHvOzmCHynwJyiOIUWO62lyYmT1WS6m9FNZYrL9if1Mf4rC3P1fbWF5KzkIVyA6Krr+NpLDnLHSAbxMjNsRLUOcOVk6qV66pmxZe5fSXHRJnbiP/RGWqL5qcBUgIOJE7uN62rcSL+M6Tk7j8Wuq+1NtjOHbbZoUkr831oWbipTMfTw8ztkcEPthuh+sfilGtGSgza0yQIYI+C1Xj6IK8OOhhSnPz6/1A3X8BGj5jobPTE/ZW7yp+a7g+/tMQZrNwnpeaq+G8hylc+Y0VVkJbSafIzjk9oORTkg6wYclBN9oGodfge5+C5EUhQpPPUeIcQj9OV+kCPi68grbETRGdU908HVkUlBF3wGWQb0UAQBe/C0n7NmF9zqVugsTnmmRH+0NLXS4tNV9AynFWP5/9KCfSDLSFS/YhU+wXDZyKKdOEVB+QH4QyxuCMP3JnApMpROv3M2XOV71D2vC9b92MH6VUYbSoMoswOZ+w7A1YJs2Zgx/pUzzq9kkfGFAxQWHEwQRYp7HfF5nktHL7GGwqbbzWK2/IyfeTM2bDDV5kqukbVTOCE82D+dA/kALGpgfdKURFBhAmd9dLaxjyDZLyTvZZ4AmEn9ZhKVxwOPLsIekm7BCdwyn7QNFsZVlyEiYTSfaFh5QjbEBXhniHVefs0gmn8smPk0zmsRx8VWJEn+o3ktPdLxqWUSfE3ZTBVgBosCmbBR1lqIXxSwEcGzHGLig+raEVzmIJ8bdK4VHAkYt9P5JyW/ubK829wQAdyVFJXj+///YOrXxdWFBoHiA8IJ3cS8aVf+PnbpC76TtjoxXAFds5eJP7nPZLDrs9lJ/O6TOlb+LplOfyDoPXXQZo0Nxscs2hDvxSKtvKleRHPA8DPxC72d6JmdQLI26kbf6C7bCaO7P5uWreJr6QwMfk86/ImT7kjAYIeE3mbPHz2dVcKfgZKpBbzPVWVoAtz7bg0IQuyXWiDgELPl5mOkkLKtM5HwhvKbgr/lu1ScGCTZe658PbSRyNcE7aDKhfaA1PcrcPdekbs0mfVVAyOLXn9Xaes0OnoJyFX6rCj5Nxqg9mJtRbkbbrfFbHym5xnHFNg8ekda4a21IJF7zEc1yjwAgXcmC/7wixHpe83DRepRzOOanlt+lf/UKPcBoiww4j1JittaxJdLoskbEj+mJfoUOxl66zvabLvngSbhyfcURJgn37rs0zhsr0miBubD9ko8Pq6EL5mKc8a35674nZLEyODL5K4X3lkcyPFCrO9q4toUo5dtmMVZfqAOAl2O7kfVCfG2z4I4eNrO9SBCGC2O/wCTfbO4CMaB58fx2SuRlWdS0+QOqgHBy97Nn8FKgRkNuttOZs7I+UBrKoB5cXfwDLUdDqMhvvt9vZZkbgGbZHEcdz/f60aZZErMFjfNDr5/Jw99MneDqKj0KN1EWnTThvTCx36RSxnyOPePP11qLABoxgJ5kbDNyHf+uS5cidwg2KkPNV+clSqTzdDTEb2ICjZz/Lb3saQ/6ucfoWjh+OdYV3dlXxZFZZnXHNJoMgAaL//wpINX76A+7QLipp5T5BLh36uh/SfIAmA3gR7pQVvKi7anZ5KE17DAzX95MxVlxBeupWr732iMhqm41p50ZNkRotrAjV+EScrrGDSm7vb/bXmtAmx/Pi5nhHiKmRdpl1K3vxju6rtWK11LBY1oUo46X3ToTC+iTzkRPqX18Tk3emC0opgpgpOrzWmLtX4sXCi6F9O9MjYv3nbPQ/XK7sMAuJQzwCgGudeJVVlk3DtAr8l02BT9Nx/IHKpqxrwad711+aLHfKm4/kl/GBywSkIkuW6Dnc0HdNcw5CuxHjHe7c/ihwkykDleM7wZWt2WPrgjdFtIx8d98B7Hoou9r3LNq0xKqudoYbjt67jy3aXeI/E6Sufb7mO1iYLIrz3xtEYJXELxein8iAtQq7qqhb9IlSQXapJ0SAt0xu0wJHBhSRLRFs1//2Wzo/ICcFHFIL/E/E6t6dWp23FuotN1BAdNdaE1pL+AaQ4ccShmaGedUYDm0t2MPQTZlE4MZvxBQSCecOV+czMw/3uFoqfW0nKkLK0MXLFFSTrqpRHgmupYIfh5aMo17Qlgy0zk3AcZ9gjNnPBAm9YqSDx+PS5L8oH8uY8q6WJNUw4vwwAVzzrDxTibmlYtXFg6qH5x11dEvJBIH55258NcVzrn+fcb2ECqkIQiy5Xb/62SShKPYosCle7YBuUhXanPE51KTNZaoh4+2oLIoS86KVpdRAeUp1iUmgmpiB3+vZAF71qKnmlaUkgGlhzmGJmpDyN3hhxOtWYeKMRxu977XwlT+mEvKJVfbODUm3pkJlPqd4zOYdRghjFCh+FxGtaK1+jMgIvxCQLTlbkrhoCuvVPh9gtInouBYG/oAc1WgrE+ZdV3PV0JkdOc/sVf7w1GTLVeqA5euWs4+v40nK5JMwwruZfNOMc4JWc1jBT7GeOeOgdRAu6R/KEhst7ByLu908YiZbcolT3XNDPraeKyeTWGZQ1DxWGXdPKWmmvl5NwlMbT8KcZIoJRjtyrpg15yUsXk1sGnTTItAm4JMleZF9oZ/vBPYbKTeG3Rt6zm61EEtoNZje+tTzMFXlxhn64CAYb7VYBfiu7MRewgJBkItjwpaG1WJqvwF7Bjiqd1oqBkQdR0427klgSyCz3v9Lw7iHXbJ9sFpYmjZokTpzc2P941rOLipUlJjbUnrtCugLoAqtiCXWYC/do805A2x3lG16RQrZS16ISk0aZH6II7udvyKi6RzkvbNVzGDj5jK0s4ZEuG61GPKMd8Q2wuQbOpKSjPOTcXu8+KmJBeGtC5JZSF6DmTPhSHf/WR7sX4qfuERGV5nV664ugldjwou7gyQPzE7g5NmqpADGWuucQkQZSRwC3Ew1qY0FJ7jcmh6EmSgTAsy9vSTvv6cyPFGePYnTJe+kqVX2xykDK0Nrs0Rcd05uKiaqYkDKl8NfVP4TGXnens4qrnahEwtCu9OizW1UEg+47gkSLa6nZNswak0F3qi4NpE+ToFVca0KF/Agly/XLJw+YmGxYF7KPLJp+5WoJRe5/7Qor+1DkQrBJWYecyWYEWia+8Ypr3kHgwdV0gYwcuBlN/OPBPlwJTUHre8mQjgFC1msgQlTjodPowddITlt3zQnZYWFa661ptuovfbChzaXBWGlNd1wRHWVWi+518Nbn/rjkj0ful0XVV35diaJc/TYOtt/tkPMs81r7R4KtC8lm4RuSj5HcjXtgfkJCpoWD3FVytN1ezYQO/99udAaXi9GA3COiXbhrSL6IlWwMtlG4IUjjCUQpcfJpEJg7u1bg19pUiNoXMrLyxaDiL/h5FN8jW+0vGsiJXRXjyD0bPBQ+DYODltWccVWmI2+Bz7orRRAvN2H7Xv8xtU/DHzBZKkJODTmjuGZnVfz1RAGe423LHa6LzPbVd7cUepkwefza2hJB7H0ovTQiPooUOrYTnN3drhytLsJtmkNzvNi6uxaOF7FYWJq7KgWoPUD4BLqdwndye1RwiMHk+soqNZ1jKgLlB5EQ+yrcGMSX0jqXr1YNMITiSEN0YNHG3vk2C01mo1zdhk0bhjBno9q6YmIXw8ynG1UhzX+oPqwQtAHFtRr4kzTF22Jt9n3FPGb2Z3NrWKMXgQb4QN7XIiM3JSxndIpHSHW5ldSSDUqc5um+XdpvyuLUs5oqyWuztpNtXq6D9IHMyJ6VKAcPJCYu8b6SFrYe2eryBbm3bMODBgF3ILPwPtzSyA3TN2fcDBgkU6/I8esS2iC707rZAyoP+a3iw0zfqLvtrgIxo6sHPeAmg0OEQocJB7sPr4M6mRxJEvna4E1BG8kFZed5DiPDLPW8B+/9Jo4nJrKx7RQx7akLJGR4GAIliPLY4GXr1xsEhlJP7/5MU9f7TXhgon6IbU05i6vj/QgYKxybsn2jZ9Yc91N00twrjg+gCa6mOI4TFoYx0oWeXInk8kzSOgxv6I+Sc+vLuB7D3LVtDouqPKeSNi7w01EakQE5YKSBDLgKpD0ZKECf/8WUGiQzn4q50p07YrWAYol+XNO/lnqrvOl//obQ/zZ2jIYTt6PtwDFhpJU6u7ISWc2rNQ4et8x2GV/lENE4nDiBVgeQ3aN8P87gQ4nbhZJ0XpwB5Hj5vPWlRlnHIMy0Tqxe9a9vEwYUm0JH936H4QimNMZCx9lMw0VVFIHKtTqoyryH8waTRFlhgbtSzL97HwUP+nXtoZjh8A5/84pEeRCp5suCaC1i7+4b3TWmBVunfa7K5uUCgeiEY7s/GZRujKU9aRfRwSm8VrxFD6+Q0lOKhgVffNVBKcCUspjX2pjNmkSgUjvKRo8zbS/6uc0o8yb5vNwkyNzs0gCWsSl3OtrHQTiBT4oTzi3PREu/KAUDwpQrWr3I8c3Ljrw25s/tZR7SBsco0LDyKgnmattesxsVk3jzA3gnIqqV/Rbba1xuFCkfVjOmVtg8U1G2JY1y8ss066tIc9sh7pJ7umse8v8NdL9jxVOIhTYL037o8YR4bNWEJaH4wFCNqnMh3t9qOnCvvVUgRrNLQgk6ZzUegmP3dwIhBP9VVpwpxa7Y63yGu+p+FxGZho0ufuhIrBRnJOv3wBDCthKFEVElhtx6sYBndXmMm6RppinLKAj4FhMykeeL7GMrTmj54DwyuXt0DAfFSNdtRZy+JQwtDFAH7jdf5Ht+h1u63NNYQ/fWgZdckdOsvAprTqLnm3tk2DXmavpckyWr07ZJ2PSKX+h9BbFvlENd4P1ircUSIy2DGg7dyvrXOcGDBm3aJV6SRozyWqp7ctCXlW3xVbyhNyLnyFNOnxhrMee5O1BYtpd3sDBaBvGIVo92ADrKhZ5rNhB0vWqJFqpCNT5Dd3R/hu6MJHa9fx1LKsKcerqyQv1jh7j1LyYEqt7EzTKjJ6V4bz6lBExegug8zXUobjQ+XTLB56Szbm0UMym3FJZOhquifUTGL9F+jejfRV5f8cjCBG+CDjIId2Skt/iIX1ANBDqfczIpV+SPwGBgJx1magtIwGjF4nMMGpAjobH8s9+jXC4F6MKtW0txHrnK/9y5xyB48ZNyoQ6KjzXu1iVWrW6guOcGfafk66RFb3phDbwAs09H4rQxjSdtQpXzlyCrCAcnT46Ndp/4NeUep/KZFcaHPdxZuQlJUMgwtQBDUbjzoBjn8huNBkEVWvXO5tPPjh/gWgJi+LkTRn0l+XC9OsAeb9QNMqg6c+8I3hXvVskwAhvfY35yJxUr9vGQHHoHCc+UP8lK+maAePvcsbNmnxjrRScLmHA/RS5AwehDa0VSb6dCNdw2z2xuefpVWzGgiWR4IK3i3FF7A7z/YB7vl0spUHxeFJT0+0v3UEoRyxHJyCee04dQ7yT7fHp+aDaZHBCFwCm0TcWUxBNnefjg9y6tyzep0ispQwyU/qwVwqWWTJmfy7RJqe0advZsU6IqXwGIitMQBCCjL3Ny8IphDck98pM/ooLIjjM4yGu/qrkSbzAQfXf3GQZP16rH9DZ2kbat6xfw/wnrr+bhZb27xNyNZUojKH3ZMOAwvuj2SAry8NymAyuimQJ9Xm14xq0/f/ki0rfK2gyD6AOfVLN5i/DvBCGxbVH1AcYoKY5BwHVkcFO3iAL4kdW+sxhREyTmRMngrT+7FWGUCKVTqyrLCsBPs0ntMziaSMKcSyXBf7Y7Zn6+28vkUPTFp8Y9dg9/NKx2hNyYTetgqR79jp+5v288L42G/wvCMZjgXtowNFeTZai6qE7irhrfObOYvufWPl0dEJvuTXBCFZbv8Qh3IYyzO854m8kynXFTTsRAKrOzs1T/ISbcUztdT6wytBPj5V2t+MXJ0eQmwJRGvxIKScXoD2d8YCYZ1SyLhOusE1Cb7LRv9bHz74VXwL9RnaK9SOMHk+mstmK/T4mu6o2Xmxv3sr9YI5s3L2BCQORGKcVJ5hqSeroC7GaHEl+qP8UCR5cTK+Z+bT5EvODgfKYg4P8dGfOJKsViDsO2YyHFiu7zvKwuVnX1cr5g7jz4reW7Ze81JlZkWxCuJZbNCowSIt/X0UMWs8kcfaSPNRS3EjXu+ggE5T9d8I8spFVj2oKjKceaxRO4wD0j9jjzNGWAMj36QnaV8goIvJtqTok/I1q0TUqanzx+7Zk/3RLCtqFFitQHWBiMwsstyXW2tKVXDVwA4oFDcm6z2FJUu8j1i6wkzqqveoT6gmUrsgZkR3q93rH8DMPB20ZQHXcp2kTEsf63aue+zXhYG+692g5awxS2vHQBZ2rMApIp8mkyA3ZkOR4ljzqd0szH/bw/wAOXWG7blHFuCvMretaIdPx0/4ZkScN2V5CUvd1a6BaiR5EK4sf8TDQmGUygHOIjssHnmRqNoCGMCiT3EjNJ/JPeO1EX1/Cu6eQM8mK2jPZkqV4K8D+IR/2EGUgI3HhfqacVG4pw7t8rtGCGomPEnfy/4B8caKxZf4th5bS4flWvhj/Y87+U415PDGiG5cBwfanaj7C6b1ahvonJpNW1QDnYv/SNsg9Mj3usHPBm9vgiKSxOlEjNsluPaMNcU1e0mLvdGRUL9p0eylOzxuqld4ALmW+vDCjNUOIcN6S+7Z1GIcXsrBk1cl7JLqBMMBlxDJuDRAHXeVuz3EZbMQEZyTBaxhpIEylLxhA3f7CNeiRXc4YIiQl3Bvz0v1pRQ9cAeFD0kEC54V1klr7dbqXxEA9G+tbHDBXvHaRAdQVRyjwLvkxpnZyvXh0D+mGEERsnsuEAxJscNW4uD7xvZE5fgIwbP0jY5maCkzANavPWK2GC67me+Z91TYBqpr3tJ8z5eZ+sPMybI+X/wbFIXW2hrq1uEBn+y1z8jdkjHRAGZ0b0uyjtH4FrKhYCiXzLn+5AGZABntR+H0mTj6EpXPmYUdak1Uj4Y1CqcamZQ4uR2r3atBL41xS0rGJWjX09Q2zKJtdd0ypcj4ALFLkXJHc1RydRogro8cuv1t/0obMT2x5ek8EJbbqff2whrHQL8WL2vqZeF74JijS7YrMg180O4RD9JddEo/VZijwdKTei5mIBC9YnNwyOl4KHt5I1rbaL79gc0gUWRArJABr47CHO4sYh61VTRryD4IxFVsYKl8hDONmPev3Lg1mTJmh34HDmAnOOcjFf4XuLi4CqwzFGrwfIOpg3eMWNhv5RMyKjAO8USd/7SGhzUbQ/lVauZoKoTeb/tMGH8bJ4QLe9lS1Q79JIyKzI9hMSavzb39GAKMbnHNbN9Vr6pPb5rjnUTAacfWSJ1LRfD5YbsDNOlatKfaLLZpWueB7O9GXBx+7NLHPdmq2uoWkKJhQBH/hk+HDvLqzGUtzYaZeXlegtzZ3icBH1FrSKho5Hv0XbEKlSqodVBDMhjfgQeF06Q4QY4VL5Lr7kLiJxauT5kL9rd8jOWjiAVaA/jAA3ZYTLYjKbl6uJvQKoj1wk9AlvUVdDRibpZE5vTFESZfzFbvytKcNX/MS6Owp5LRaBrvqNm3S+8tPqkyBTNyCuxhppdcJmw+HXiDFWcJeRTonSvMw31Yq0rTZIFqxNIjVCH2TBQj85OUPTPV7NlymcndgS1qwTC7eXWwFD8WrBpnA4UpX+itT/9ZSjavehXGp3j+rbSMkuQ6Ev1YCneVzCHXtBLkhEF7ye4Jg7Wp0z7Zn1ZLgyxPL3Zx0jn10g3rrKL3BaHkong7+Z4CDAdPq2sCtY61SLXS1Vjr9vjrVja4Q1txknXqm+CWyducw9+LNIsRKXi1h8AitSyZTl0YS4CwgAX0Yet905gU9sIVoY11pc9KU3eJeJtIm1cchWtfUDlTo+9OGSJn8eKl9I7zB5PPh0x0i4XDcB+oz5vZTprAiN3iAI1i0eQxxYf39V9uIlsW/y3grcnOK4bZJG/YSLSFQquR+30kNUx1scM//TdvqzRfAAOSPIJjFx+y0wh8YAtHHEpG8bSsOz78c1UzfKsP5AAIAjkPby9o87JT4h0NpY7vv7no30t80V9VKSRQD2KBgNEWK0JRcXF5lN1IFOjI+jUEm/buWriTKgsVRdze/t3t9oEOfx2/WsRtIWZJBwQUbOrGGGyY+UQWcRGx8qjce21zp9ed/i01FZ2WZhC1RcsKQT8p1irVORdMaaloWogV0DomV02R6Ao88YHgKqOekSiKLt5TR+IMcctlvFS8TEbcIjEOO/1Dzhh2jjYzCaJ8pCS8z8smebHGXuSujpIjGOY1fyT0oWacNm9brD1MVg4eInU/I1m4aDLX7vrrccgH7rqwabQl/GgtFZZsLlBcgSdIOjN2BBWSNGgqtc9p4hLJd3GI/IfdgmnC3qWTvVX+gtsSN1fqn59yXjJD4Ii+1q+TdWWfx99pzwqaw+db/rmUV8zxu5OtxJ7VWZzgrZjEWzYaLtxxB2bWbB7/FlWlAxqpsDMhWjzEkwxqR3E6huoOB/HQFhmivKU+oB2yOIM0DbLaU90h1tkurQje0aw/EkopYpCSMxg9VazrusuccHseGW/gp4oQ3wDM5Y/zQxfRc7xetbrSzF65dzJgcFfR4s2aq9CvmZVlkNWQpoe99FN+HTGLK7+t49V8NvGyMTd8KszbG4eXlaHznpDj3Ls5SC79akFzucs/ZmRSHGVaYLsx9eZ1BwnkauA9pc9X5TpwulU+IIa8gAImImpGP9K/1SjKSfOqbQBzGcS5c3YMGa63g6N2PpQx8/ylP4pyh6uG9lpw2MVhqudGyn4S8UsRsunHjoaqoQol19MgBtLWW9rvU+Hy1chrXkeYCOrXpte0pL/ODgYD9CQ7tqjxzFicZFZekoYRultTnQ3shxLxSeKxFKddORKMj8xmlRtLg68PUobGzzlM6kExl79VgNCEZggAdzhfbdZuuFoBiLvl+nITFqV30RgAXa9tcvwY952pv/25Fh8GSXgmBNKv+U6zhjmPMJirzhdQDGRrqXW5WjUsoiMrugiBrsfN5gYazRcyqdo+2U4G/YIXl5gWCdn8pqQn8db9aoyMw29gQ0xUIeFNj25XHZ5Hs8vvvEcojvAaOZLPxT1ixGlOfbWv7MiH/YlBdo7c666LFuvA3YGBMV5rp3+Cz5ZDv09ynIpGfn8foXuN0k5id+dnu2dPLYq+7JdxFmIaLTiIOXJQcaBVR2RXHnqnkvq0Sl4+N3imc+GGWZkG7urtYtDCgtVJJ1kyGoecvVRzNADV8JKygSKDRY+M9+edk6uq8zgy6p7cVqvdLAm3FJRaVK1TJyLpWOJCwjKTLRcdT2L/HgNC8iFkgzWR4BRehBeRWVhqm8zDm56q2lSuOjfKd6m+jZViG2cLMZL0b2Ik0g1I/JlJvnIeob74tE2+ZwKamKPYyTKRW2aTHuRmJCdK1ESCUAKkMbRLa+uGjM4TfIRDJwC3c/cSiLnojTWj5LWw==\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" →","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract →"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract →"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\n"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"Why","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" gathers","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" context","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" &","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context &"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context &"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" brand","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"db2RfIwt/EgN7Qw7W7gyu50pUYtgaOPhhUk8C6UJ85BEFwjrBoRLCVYHwFKlrlvbd5LLU3crSpUtkMwezu8tC7OELMqMgl4f77cvqql9yCqIwP6kC/ULuZhqeeBcgqwGa+QRh1QuTT5atClVUk3/pBNRXF+tCIKqQtKRNXb/R2HRcHn+MkmmITmLw/csoSsA0bBdsbUuf6F9FtUM8cbVWOQMbo6lL6nevnqWOUmKXqQeuwWCaNH+mbmgu2gGruIZAiO7lS1NmcMx1HblJj5rwhvxfbAnWqUL8bc3tPYQBT2Jwtaz6GTkGzXioY1yK19rjyXZQcXI3+gomGjZvCSemmSXjekS4II3Cp1ZFw+Ub2QTfk5ih94R5nWUxIdC5lbXyVQGkEVlhh4mfulIkwuSvKAAhzfuFeP0z9GM8ghItDrRj0QCERiukneezpU6gh3R25N4pD6aySB3LKRdMMWTIermlT96vRnAGl7zREFzeoAvxZ/Cukt1zj1hClPbz0ZIBuz0vHzF+Uaonbr6PpO9ZNSvUmDl8VBiqDC010js6ywQuRUABc8KmUVGuKXiPoirKTNlk8gdl5mis5hkPfAIBlKYgLcdMa1Mpypi9VAeVgsGxGCKCmdrxL1FwTVwz+mefenlsta1hSpybw5u8yRkdUFyOI2Aeahim5oXNDeIJjxL3xV7CGxvlq/2/ZNte7KZAfSWg8CtjJumFx+G3EaQlBLL9Py+ereiwEy+9cWZX5AhsaPrICf+gCxaODregS8wPK7CUpvtjG6hcCN57fmrVSzLEkDJm6jCADghImUrf8U6khZeILQub4NGUjUa7/KWkNUkAVIZ0ibZUMs4TVMD5un9AFNcqqnP8zoP1Li8RebzbRtTgXoa/BQogHCDAf7jFBzz/YFxphIEtWhxnwlJEfntUkvU/JYqwS+5mEg8TpRtsWZ+IaHsLtaLSwwZ71K+rAwOKGSoDcbPvUzR119j3vaVhljpM0GTLfPt07kJqHEN7Sp2Q+8PgG8MecrdTVAYYNFKYQAYuU10WZ9KhzzOKGu9Ziyd7pexl3ZDsDFnm9jS6YYB/m2tT+JybP3MTPNv2GTNwcf0A0hpc0iduC9z2JNeytRqxmLQlX2myRHaEYt8Z+Uen8GJPhZ6dosxCa2f64dv7SEYJrW2+d+AfYX2mckOP3PbQnLqJaEnKUMtkgDLLrkz3keWc2MsgjgvEE47zpWliDu1nshlBOlH6fVd0pMbyd6Ncv/h7NBAzPd0uJ6HIWBhX5vyxSBvGP4nM86xoYyfsADF68NEYhGBCE5pCDmc3rH4QrVVeWYkUHXNGIdYDsxkvt4i8jt/D3Y2cK5KMmTRtnmdU2zp0N5LUTKOHPvuFMPbaEGA8lSEEol+F24RkFFVywB8k1k40pCrmKGHN748U4iJEHXKLDBgFBRngMd2HBfK9W9Q9sPAAV8kd3o0h5pHqdevHFdA9a+UDzeALwwtz08NRx5Rfv9ZP6Nym6xJdQ6qmz64meaXSKtyydUBJYrWrCVmd9KV5MdACbLL7Y2zGGYUvgunLQkU5Ixgd2JupAt/TSfXrp2fkCaeIwlT+4Yx8LSDuNx/q6/CiUbniZgWbZOkRVhVFYS929EI5kYif1/cz4YKwN9kLTsDJQFryJQl6OEuqEfNscxeQtR93hVIqEThhuORd/tsJwXoCOh21rZyB+fi1MVqJ0G/MOSpuB0smIOk8DY5o9gurCO8KGDPNjp+lT+zpz12j20dzCtrCsdohBDL48dFgAzPxkYxI3b9RejL/ilrQ7pdPYOLstUrIlYK2q5bxOadd2SsT1935UKDUlIWr5zV63T88N5qEdsJ0EdtM/Ke/1SwvKduyasy+fbNNa17cMtRIF4t58XgriqKT1Te1ljUOb1Lq/yHGL0zYn418Kyrob5sAgTMrFGgr9NaNj7ESlhhQqJntiwBoGCgGZI432Q7Za4vbzyoFodKTK6CgC2gZWofK2zFb1QWSc5vXdQx5Xk0xvt5eQun5mWOQ7wEgljHYIVfo+NSQhnS2NiR/U3ae9Cwmi/NdG3gJpvK3fLydaKAzED/MF+nvG4xcCDxXRE7suzSObhiKrfYJhbahW8WWkXLHf9/+m0Be+nVA+kgOzq63UBVleN1c5Et02zecEnc8jA6vAsshyD6+O3WVbOxxqAo4l8P5udkH1EIFfPr1iXgEWHif686shj1MiHBcqiq5+vhn08A6VHbZ3P2B1O9T46jwvWvSQabZ7YvQ030gMmahCcgdkWLkYtn1ZPBwqpxs0J02lBG1An1kj+kUyOxu3+41yFc4UhTJ+EuYqY973iROZDN56R7hWefrxX0G/0WpkHpQUtstq0+YPNee3stS+8y9pgHqsZNdfCCJYib1IPrq0i5LsK114bWmLBQEZ1tVPHE8vZzratOTfyJg9TX3CJi7Afk2YHoHMjCzaCyOYnH3ym2vaRdzOwC2S2I06upCM+HQAqmaRMxqF/BLTeUsL+HkT52Bxq3UnZGQ1SWUDdya6IA0u2p5TOu5BFs4/A4sc7AG/Q1BWEIXbmskVWlgk6XJpHU9T+jO3PxPsTWvQGRtS8O2X7dBMCbyY99bUog+C5ljzSbPIBuIBqZKZJy/98dw4Lq5ZCBisGQi7ho4YX9svuKaCOs+Rmh2wwYYq6Fd+xH63B8DY3VsJr7Vw0qn+qUyvIzUU/S80dplg02jdY4rNM3TqFUzy1NPK2jYIy8ZIiddmZR/TlQW7KYszpa2PMPVxFrUbW3wiqCh1ffhM6JfRr22yW0L05uj871nZX2zUEkccgDC4KXN9AQ0V/WKP1of1ulo92ldHbUk1jhkN3to852uH9JkpTHniMeR/UVnaZjpalvIcexXENYYhuUVV4/gpMMAH+CYQvINqnTMcONVNR6Hrlob+ojMBK6poeGo/r5q6MnDnOaVfeFPKspRT2wA5h//oDIr3kWg+kfv9/nv+9ExN0AF5ohBI4xRrUEoQ1r/GoSn5qLYXCb+Mta3YKsmWPsMDaQrKRGz2YPA+XmByUiZW3wgpz+o7lJa4aWqenTpabgHFpzlMwlZi9/sCGPiAEKelI87jBDgMB6bSFOAhatWKHdRQecd2MNwS41st8l7WT9GhOK5LNBvqxLBeUd+5PYFIS7wYXa/j//5PFdt8/OjBx/30b4aI5u1GC1y6RKwac4HUXLuiAPW3bsD10/PHKFQ1Vo+wMNyA5yy1P6geHZSxphHGlGgXOQKhXou8rczI5PFetekzJBrVPRIZwM0kEMDz0xsbNDwmAIlwMXvyszkukS7qdJ8dRX36hWonMGbJ4xJ9sqCzeQoznytO/TkmUa0MRtCM+Zul7X+Y0i6In3qsntz39648SSjowzS+0gaXzV1ZJVwoboSefVVNp/rODgByzJTTv4+MtV6rJUrvolFH9ORQPhckIDUNFE9wiWMSENYEs67vTmFBcXIwdVRaPf2A68IhG4IPNqZb5FbkGlKdzkltOSJ/dSZOYhOeEA68dCNLmsAvu7xzOAR8reMJM5zUStVWGBuMzDkjYBm4hnANXZ6BXfHDXNMC+YZ+dFvRZXcRwra1IEcIP2DanKa/NVGn7R8iy3Qv7qHJhPZtdU+rq95KEGkZBLWfrZKwVlMtZ37yM3XF40OxGi8moNJ30dovwNdDLSVtI0ekW5i1yGXZD9bXutYm8TZJBoXo57zdVcZiRQFaMYVF2jV+HWJEzx+FB/tUwhZp0jBSiAk2pr7HGbNmV3sxPh+VIVP+Nxj3ztqbHXY0/wVcWChp3OYWuGfKhaqymkFTk3ULWCyugix1BiGRxp4+V0HVAgI14X45u4F3HSGeSzAkf+F/vuQn1NDDU4F6/qc3fgpsEUYjDXvftZIttL6XSmugIgWXmo6RSIuhXs5pOisBDJ3fc1jNs87o/6TVrDY3wlrMiIjyIt7fec3b17NtutdIJVSsjd0/mfrbMzzvnMyKK6Q34lxoLWwKIejYikfPzwSX96jDfx5Uz8WTq9nBV4TUQpb6V6Dd1xnmDnDT5qwjIpZLHwivnaVg7v/2EisLe4lx0622aTAVRFjvcI8AqqWPiSVImj80UIkcOEdJGkVvjcoC4Qb/lzMZ84SCBBeCilYy8KpXJsK9Cu1tsb3dGBR2GZ93T0Alomumjth8xMZJF8CRBsQVPEuZVwzQjw3dvleELz2cT2PtQGLA1+t+QiElEBRYrW7Bp+jOuNu0blzJbiFpj3ODT7UrXIal92iWflN0N5AakzouOuQaYoFEtJTP3Lc2HDLfWJkPI4NrVMFxD3OV08Mbo3jPNMumy3VmjcNOGtfWElY7/KrlHN6yfB43NBaYMqbArIOdnmkANMgkCxoV2xbCwz2xC9ksKx+EtKXHmBp4gs2vxlAzBIrsOV5wjVEIIwavrTvlBmnr7vENLak7usnBLPSIXYrkLOYlpY+7J1Qr0Uc2N6E+mtEtqyb/nDAyyWluOHvOPN4ZfsPByhye8RD3MjoZhPsfRYY8mvZFpDR+cBXwkA3MDDTe1ouSfG4xYvg1b0RTYZ2UEIZtBWXSWGysSRQLGbBOU6fb9nl5WkVKjPCfbrckOd7nW05FHKCCeS3cUh8ILs4osr594ti/yb2gOm5wFVeFltpvu/Ojp7Fa6jAPSDIehV2aRdSI92YQEaagosCrVRDg9GCznZ4c7W1n93oASyIHOyD8n3XF2oAXk84FOVbQvklpKPt2ASRmlY/p8Gb41rxC/ZEDLxpx3cZHOUzr1Es7c2jG2OKTFFauAebhflGbZOSBryUDamjS5Twu/NvF077a0vJ5pyZBorBWYS5Rb1AjXuCE0JCa4TQHy542tlll35tB1eW9FC6Px4Qernh0Kkq+hJ3m3uWe867VaBcFfsWAaUFjtF2QEpFuZIswPveQIeYCcVL4CCZZqctMoeOXAgdM5KLuGV9Xp0r/aVt/T5bFwBMIjPVFtSr1kT7nvip44jV0/SmqmgWtVupfPqyNJqA7EVqpjegNWcJ/R2zCg7/npXSsc4cGQU47cVz9sq8qQAQmZwRSg4Ow0vLfQNzQwNpjEYu1F0ZwQFloW9O56dvdg5EWHsJL0FrvD2C7rgeX609NWSNSD94VJ5jxZrIw4IFmjARpp5oaCrxv0bHCygTtm1YemZij6KqyAKW6o1he1TGACJB0OMFVRFoDdxaYFKDIYCazQJY66nf/MhrBuO4HsgceaSqlO5Q/P1XbcUbzx4wc+RoE+S6qw3DgzBiUBzoMwgxjuT/k3lyOTShqvdwi9AgXa/IS5sfOC9LXQf3N5bvhNOE+O4Qd4M25YKz2gXm9h4jnlMwZkVFujrnQO4f36AMyY3aXe7/ulaY54RD65G7TiXwsMH0YQ2wj+gaGgEzzLjXCkyQt90ZuCqC8E9c+XqXsXikAHYWDM4Ga4YkcXsKkPfre2IEHMpoI3thgHI3PBIxxCQ/UmoMJids/K9+jdfCfzdNatyv/5xpdHqX49b506xEmFMSROas89a8/96r1KZv7sbvSZmmnrfDcmbpwmzZV89O2eLXAtdYgqp0jHlaYVUat80gm7Av9CNdHg+v/ondgsdTiFWXig9mSUGtGenPMs4aG892EC72CvbCyHW8ID07iQHWruRr5iB+l93qw2YHi3i1qIhKIfHIyh2WpHHkFIFpU6lZ8lz7xAuTK0y00bsjq7nJ+iT3hvcIQRo0PpLuF+SeUnrU/d5mGQRm2bXvg3qlumacoRn5LWow0PE8gWFOM0hLJGBE4nPgLL7KFtTKq5HSRy1FFLGtANFNBDy17Ct4lUrLi9CFIxDJufuPDef28FgZjG5Q0wEqejLYiHdTvoBbvxBB5/P6xnpTnidLiyQMRlLRbYUM+/zDnO+gMb9/2ysEdfRbJ8l1Np9Hh8uyxd7OWOqnUpTW1vbP1gybmbvApE/jiHJcvUC+EdwGxc+abBxXInQMWg5z1C5CoDSi3qRONpBSX/Cwloi4xeOtEjENpVcbEqJ+5txEsT5k41x7X+IOXgxN2S4p827xvWJWWmEdE5K1J/LMg6VdAUcoM4omw+jhsefzbbXseAoEU8goxTMMz0r3VXiEvnYQr9uAgGqDozv1Jdow+MAy6+9XKm/fqLEfHEjpLLXkpNJAuaGim5B2pEIFtQ7mVlnZcdgPbkzj1/1sicqpajorUMPUuP6i81rHY/+HSoTfYAleuZNearLXyAJ7x1c6Yhh0vD+TF4PEw+cxlnFRwi6QuxA4LPv4EbuQYM5eN7WeGGulUvQNiO6YLvYHigKpPQ7FiVH0AHbZXR7jzOPgsSnuDsCJJ9wy+dF7DPdg6g6Id6H93qXWTXyzIhGIp1NoMKXoDixBmdwa7iU+etDqVQtkwyE9e/8MIN5uBIvFcZ70SpV7QWDteqz7Wgs7aAv2om62WPKctFPo0FWizUz6kjddbGD7KRzicxjAc0G/2H1tfbwIBvtUXNmYb6Lz9Gg1fYjCrgiBlkDYKNFlysjn4jrtuMlXnbDg1Rs2b65p8AbeDSqDBGsMjd/UXm3KfjKZJaXtX9uzz7CPRC+W662mMgC6AUGPKZL/dtwTUhR1xAkzkSapUNKupE/f9IcPKWLMQ7ZHOlJxvzXvTF8Jjqt9ecB7cDuO4h4vs8glGg9QUk6MIx5SRMPKn6FKEa9zgDpq+2+iUla1pn/MQcHcfR9tYB2rQes2ahuLN3vrS8cRaYVG49D8DLBdn7b0LJCrbMYhMJtIEUrJOVVhXUjRCzjvZuWj7eh3tvfHL/ixL4BA+rDLG2w0cu98bvXnulZCEgou1iQPBx6YR+bLmS6gYzcENwjaskixqqMHzPh/QljCOhcT3riJubfd1QVunyi2705MC7Q4M4g6Lt8SxCCrkj2iuxD/jtsy3t/PWlyB+sVTmOjEUp1Z4buqx8NlhjRgNXM37oGhCFEglPbsqs6vXX87zm60oj4CSNRX2xrBoDzVagwUJwKHaqjr6/N4CS5BNK61dty/u1sabimbO7am/+yuKR+r5TSO/l4MyVll9YEhuGlcMSvSV37sJZ2pgIyQviWpcD/wFRKqQeENJIyS8MHL8V24Z8L/QEAJSjFTk1IgxKKC2IexSl3BBE4d8Cl+sHU86uNHfXy3pffN9IHflt4uEMC2yRJzQT1Shc5ofm7ir5+3UOtq+ipeHIl+p16MS7tvX4/P4hOsexDLvUzDaC6/B4g7MXv7BJn+i2Y8FfqcJpQ//pKMqDqECDb9SCwT9o/qfOm9TCdnsZwEPnNRXZiL8JCB/KJy5DgnLr+pxtPeFH0N5BoknXATlAVcgOVQMA+AifpkvmLeylv5bgmwxw8Ji7thYCHujBC/UOMpvySeGl6lyg4IY2gMkHtUmuA6P2rhoNR8d5gL32li8h5eUI3Q/7cJkoprP+B4wIc+ncIQTKDB9WOXhGV988b9+YOe5ZeZChn1yKdvm8JtZ+8t2CkLID6Fl7GWAxbG8K+8mwTq1x/tYdZGflgiUXzq5JWQ1+Dca6YBAInEHTJsO1SJ9B0dbHJZRXhHhXySggDmoDmP5cTwH5lfqTbdFyrv+PpJBpACEXZ+gCJU1VMPip83AkTmQWe5ltiCM8heMUOEZaZci/XIGXH79J4b1Y+H/qSG9+mt9mt53av5TSSVeY/GnYy/eMcQHCr6dnDts6488QIzcn2baeMZslKURz43k+8wHGgCBlDw0v8UOusS5aSpSHf7al9boGAouKiVzaLwKvMes1i4+fyKpEAHJ0bM/zqYKqKzPZE1AHoFcbwNPbtbj+UZ6t3eEMDpe/7KMI8WkzoP6v3HsLLBFcepVwY5WRcXaChpD14gn9TGsEZ0bmEflje30As8DpaPXBKSaIJRvDefuBYObm3LyE0xZIHLISqUeGb9w8q+U+B9vp2u7a/f4pVnHiDh62BgwFlbwJBjWsU0cZTTQv5z1ehuJKquZpkwf96VGm0u7+qLHr/UiRNweADpINFgvZEcLRqsfbS5P5HY8MWHkQlOplizpopgg2de4atKtfXC6q3gsFVWJv4wnokxa2urlgleUuxNE/kOjZhTBlIwE+PGQBwMz85kRDLVYZCCAi3PoO/0WEb4Wx3fH+Asune6YrMmGz4Hhk3wV1HTzE2+1j6xiDIszjmD0Uq95TVAqQWD6sBz+Sww4CJ2F8N5dhodvrPsfpmoEZdl/lDMtuXFXl2q1XdJfv1C3PltE80IVwbu9opvocY9C95dXRSMGPl96PrL0+eRpcxy0mlCBLD+fujSNbS2ME8uMId+2I494t4eProErDzvKMKRXQia4SxTp7uJcMCOlHkGEYDw7/osgX7l46ht0zQaw2TRKRknxz1Tl2dod8M4gNAWCePnuSLTt77AZfq9vyyzqY7+jDOmy9gVCazeaUAeLi7lUYW2F1SZ9Ekws59PralQhDPz/ZmFIPT2GXarZRQIIFIqFY4NWutN16IMm+eoCetMCZDxXf7HC+/AWGOzuYn85gilx/W2I3svyYF8mxOkOjADcnQIYImyI+l1umtILvaJdMf6lfRqLG1xT/Ditp6Hd8A4Zpzm1JJ7QDwS4qz9IRBJWP2qMPhw8OhMYTE3vPN3DtJ2VccndwRTQ0D8xNdgFDb+ST6vuzhGo4G08O+DOxF/mOW8f20u4MMtNDjZbmYRL1PY1Oi3tjEeRTKnm99Fz70/ldCHsbQfVGruhxpXQXWzMDVXFpKE/j4gpN65RRHonb7aOV15jvt81TWhcUZuST26YCKWpDpGZY/rXdxk7DXK0EE40fHYb8vxb8ib8khVuDQLdjii8D00nXjjwcmrPNUp89jeB3uQfsz6tyOzNJjR07ZBiNJcuZYsNDzUsyul5sYnUBohlR0xeWvkxA16uRB3bj40DPYQBWY8SdjRFbUZn4gByzWqLJabNGXLHiv/WWYibnG0r9kBw8ZbdfD+zhNB7Dk7Ytp2pb3LCI4M4ZF5/0+Eu1FGDGMm8q8qD4VDoaCeugHYlPTO7r7I+ZHd/of3Qwe+39ocG8peQBND5E0UQqJIbojYXHpxYBvyIBHpZ7pIhDKLumlUexBydHw5cZGMggaOAJXTeHPbZl9IMYWwejagHl717Il4D+OBSppoPgTDCw3ttXjX5Ixmr5ZKWwY7KDFQwEpdOlf1GIYSZP5LR4xbOoZuICvw9aNB2yd9iJuTLtZ7giQ30OZ8lcR6bN3MAEHr40CcKPngfpNNR9UineTzyawdTX8lQg/6EaPKA/YQncLUbZuN8366FkDD7CtGjYRBHZTTmawE9oI+SJRfrKeoIL6qunGO0V1a8rUZsdVEPYNmoRpQzAM84F5s8/MkxNvd1yPvjCuygxRZIUg3HMS3iEAASAcDs+q4fr5/7ecR5F2SiUAmLQmdDKfCJsAdv1Q7s0ymiXspSJxC1rlPDKdPHawr+/NCZ8lKa+zbPzNxdjY+svEFox4Edxd+XzU6RHIcm8Y9ITkD2Fb3+DDKI5H93cb+tppxdZuSTkRcEi4rPUjw2/zwi2QHxXaBQ3ScBQQOyDUAu+hctx4nFy33eF162RRP93R1PxBJ9Ba27Z7OewA51G+1UBjBeIWasPUvdDVkQbxHH9O6lUWA4n/8sXcDi99Dkr8D53B28ticmuAb8KihOCxg0hvH1TKkX7PUHhVAZ4ZY39dOIORb5X1EMRXJ4MtGkgCPQBmlEM4dl7XLK1AZJ+9m3L55y4kC0haT8CZEiITm6OxCCnua3wsIDwUPRgvR2picyepn3iZML0+VTNiolR9Q4EdCtcjRiySniFvWuqkf0ZOv9gZ3Jyat7fuGb95Fj3GZ++t7w+Bl8IcUKx9ycGlUtGut2lm29qf4go0WBptHzqG3c/y2KOOfQg/+iRgjzFmb/60uUIfNrlkUTGePN6JmiUIr4nCsB6PEnRtni7CHfVjmAQmhZTtP0qQ2q0aC8JmMNhAF7J4G3F2yPCBN+fVtluopRuWm6EZbvnxHV/m2/H5jOmAgzd6ZbvreaFLi7z4EqRGdHrZH7xyvE0Bw83a6/92cN/SNCoZhGKM0sdwzBBTift80WqlRxJqXewnvnpGI/cwZev9w5tQ1UNBnZlL7o2HjRanpWtDAWDxUjnlpgq5Cc+UoSWDKVI8alSZ3zSiL2QKljRthZM12dOcEuzxdRSNHOzPK3I2KPp8poXNQRLTRVJCsUiDCBXsrwepBihbg3i3WnC5SAeq8fQXa+zyh9QruGoNowAr0FDHHjN/ij+eAaDzk//w5D8tlmovKdUo/5AfFzgtYJr4iHvV/wmt6Q66Dv9mVgKoXqqaeklVUuZrKCAuJuArQztwCqAALBBV2WRthr0Qzfzn43goyV8VAJ5ege5bAjHRBLNsdOEf9HqKz2sfSUM4cy8nqSn2VcBJRr7KTghleg6LSlst1mYec3tOlm7kHt6S8oQzyQOzZA+wUG35mOKbHCbB8oXngLxFg+Et3X8VHvOzmCHynwJyiOIUWO62lyYmT1WS6m9FNZYrL9if1Mf4rC3P1fbWF5KzkIVyA6Krr+NpLDnLHSAbxMjNsRLUOcOVk6qV66pmxZe5fSXHRJnbiP/RGWqL5qcBUgIOJE7uN62rcSL+M6Tk7j8Wuq+1NtjOHbbZoUkr831oWbipTMfTw8ztkcEPthuh+sfilGtGSgza0yQIYI+C1Xj6IK8OOhhSnPz6/1A3X8BGj5jobPTE/ZW7yp+a7g+/tMQZrNwnpeaq+G8hylc+Y0VVkJbSafIzjk9oORTkg6wYclBN9oGodfge5+C5EUhQpPPUeIcQj9OV+kCPi68grbETRGdU908HVkUlBF3wGWQb0UAQBe/C0n7NmF9zqVugsTnmmRH+0NLXS4tNV9AynFWP5/9KCfSDLSFS/YhU+wXDZyKKdOEVB+QH4QyxuCMP3JnApMpROv3M2XOV71D2vC9b92MH6VUYbSoMoswOZ+w7A1YJs2Zgx/pUzzq9kkfGFAxQWHEwQRYp7HfF5nktHL7GGwqbbzWK2/IyfeTM2bDDV5kqukbVTOCE82D+dA/kALGpgfdKURFBhAmd9dLaxjyDZLyTvZZ4AmEn9ZhKVxwOPLsIekm7BCdwyn7QNFsZVlyEiYTSfaFh5QjbEBXhniHVefs0gmn8smPk0zmsRx8VWJEn+o3ktPdLxqWUSfE3ZTBVgBosCmbBR1lqIXxSwEcGzHGLig+raEVzmIJ8bdK4VHAkYt9P5JyW/ubK829wQAdyVFJXj+///YOrXxdWFBoHiA8IJ3cS8aVf+PnbpC76TtjoxXAFds5eJP7nPZLDrs9lJ/O6TOlb+LplOfyDoPXXQZo0Nxscs2hDvxSKtvKleRHPA8DPxC72d6JmdQLI26kbf6C7bCaO7P5uWreJr6QwMfk86/ImT7kjAYIeE3mbPHz2dVcKfgZKpBbzPVWVoAtz7bg0IQuyXWiDgELPl5mOkkLKtM5HwhvKbgr/lu1ScGCTZe658PbSRyNcE7aDKhfaA1PcrcPdekbs0mfVVAyOLXn9Xaes0OnoJyFX6rCj5Nxqg9mJtRbkbbrfFbHym5xnHFNg8ekda4a21IJF7zEc1yjwAgXcmC/7wixHpe83DRepRzOOanlt+lf/UKPcBoiww4j1JittaxJdLoskbEj+mJfoUOxl66zvabLvngSbhyfcURJgn37rs0zhsr0miBubD9ko8Pq6EL5mKc8a35674nZLEyODL5K4X3lkcyPFCrO9q4toUo5dtmMVZfqAOAl2O7kfVCfG2z4I4eNrO9SBCGC2O/wCTfbO4CMaB58fx2SuRlWdS0+QOqgHBy97Nn8FKgRkNuttOZs7I+UBrKoB5cXfwDLUdDqMhvvt9vZZkbgGbZHEcdz/f60aZZErMFjfNDr5/Jw99MneDqKj0KN1EWnTThvTCx36RSxnyOPePP11qLABoxgJ5kbDNyHf+uS5cidwg2KkPNV+clSqTzdDTEb2ICjZz/Lb3saQ/6ucfoWjh+OdYV3dlXxZFZZnXHNJoMgAaL//wpINX76A+7QLipp5T5BLh36uh/SfIAmA3gR7pQVvKi7anZ5KE17DAzX95MxVlxBeupWr732iMhqm41p50ZNkRotrAjV+EScrrGDSm7vb/bXmtAmx/Pi5nhHiKmRdpl1K3vxju6rtWK11LBY1oUo46X3ToTC+iTzkRPqX18Tk3emC0opgpgpOrzWmLtX4sXCi6F9O9MjYv3nbPQ/XK7sMAuJQzwCgGudeJVVlk3DtAr8l02BT9Nx/IHKpqxrwad711+aLHfKm4/kl/GBywSkIkuW6Dnc0HdNcw5CuxHjHe7c/ihwkykDleM7wZWt2WPrgjdFtIx8d98B7Hoou9r3LNq0xKqudoYbjt67jy3aXeI/E6Sufb7mO1iYLIrz3xtEYJXELxein8iAtQq7qqhb9IlSQXapJ0SAt0xu0wJHBhSRLRFs1//2Wzo/ICcFHFIL/E/E6t6dWp23FuotN1BAdNdaE1pL+AaQ4ccShmaGedUYDm0t2MPQTZlE4MZvxBQSCecOV+czMw/3uFoqfW0nKkLK0MXLFFSTrqpRHgmupYIfh5aMo17Qlgy0zk3AcZ9gjNnPBAm9YqSDx+PS5L8oH8uY8q6WJNUw4vwwAVzzrDxTibmlYtXFg6qH5x11dEvJBIH55258NcVzrn+fcb2ECqkIQiy5Xb/62SShKPYosCle7YBuUhXanPE51KTNZaoh4+2oLIoS86KVpdRAeUp1iUmgmpiB3+vZAF71qKnmlaUkgGlhzmGJmpDyN3hhxOtWYeKMRxu977XwlT+mEvKJVfbODUm3pkJlPqd4zOYdRghjFCh+FxGtaK1+jMgIvxCQLTlbkrhoCuvVPh9gtInouBYG/oAc1WgrE+ZdV3PV0JkdOc/sVf7w1GTLVeqA5euWs4+v40nK5JMwwruZfNOMc4JWc1jBT7GeOeOgdRAu6R/KEhst7ByLu908YiZbcolT3XNDPraeKyeTWGZQ1DxWGXdPKWmmvl5NwlMbT8KcZIoJRjtyrpg15yUsXk1sGnTTItAm4JMleZF9oZ/vBPYbKTeG3Rt6zm61EEtoNZje+tTzMFXlxhn64CAYb7VYBfiu7MRewgJBkItjwpaG1WJqvwF7Bjiqd1oqBkQdR0427klgSyCz3v9Lw7iHXbJ9sFpYmjZokTpzc2P941rOLipUlJjbUnrtCugLoAqtiCXWYC/do805A2x3lG16RQrZS16ISk0aZH6II7udvyKi6RzkvbNVzGDj5jK0s4ZEuG61GPKMd8Q2wuQbOpKSjPOTcXu8+KmJBeGtC5JZSF6DmTPhSHf/WR7sX4qfuERGV5nV664ugldjwou7gyQPzE7g5NmqpADGWuucQkQZSRwC3Ew1qY0FJ7jcmh6EmSgTAsy9vSTvv6cyPFGePYnTJe+kqVX2xykDK0Nrs0Rcd05uKiaqYkDKl8NfVP4TGXnens4qrnahEwtCu9OizW1UEg+47gkSLa6nZNswak0F3qi4NpE+ToFVca0KF/Agly/XLJw+YmGxYF7KPLJp+5WoJRe5/7Qor+1DkQrBJWYecyWYEWia+8Ypr3kHgwdV0gYwcuBlN/OPBPlwJTUHre8mQjgFC1msgQlTjodPowddITlt3zQnZYWFa661ptuovfbChzaXBWGlNd1wRHWVWi+518Nbn/rjkj0ful0XVV35diaJc/TYOtt/tkPMs81r7R4KtC8lm4RuSj5HcjXtgfkJCpoWD3FVytN1ezYQO/99udAaXi9GA3COiXbhrSL6IlWwMtlG4IUjjCUQpcfJpEJg7u1bg19pUiNoXMrLyxaDiL/h5FN8jW+0vGsiJXRXjyD0bPBQ+DYODltWccVWmI2+Bz7orRRAvN2H7Xv8xtU/DHzBZKkJODTmjuGZnVfz1RAGe423LHa6LzPbVd7cUepkwefza2hJB7H0ovTQiPooUOrYTnN3drhytLsJtmkNzvNi6uxaOF7FYWJq7KgWoPUD4BLqdwndye1RwiMHk+soqNZ1jKgLlB5EQ+yrcGMSX0jqXr1YNMITiSEN0YNHG3vk2C01mo1zdhk0bhjBno9q6YmIXw8ynG1UhzX+oPqwQtAHFtRr4kzTF22Jt9n3FPGb2Z3NrWKMXgQb4QN7XIiM3JSxndIpHSHW5ldSSDUqc5um+XdpvyuLUs5oqyWuztpNtXq6D9IHMyJ6VKAcPJCYu8b6SFrYe2eryBbm3bMODBgF3ILPwPtzSyA3TN2fcDBgkU6/I8esS2iC707rZAyoP+a3iw0zfqLvtrgIxo6sHPeAmg0OEQocJB7sPr4M6mRxJEvna4E1BG8kFZed5DiPDLPW8B+/9Jo4nJrKx7RQx7akLJGR4GAIliPLY4GXr1xsEhlJP7/5MU9f7TXhgon6IbU05i6vj/QgYKxybsn2jZ9Yc91N00twrjg+gCa6mOI4TFoYx0oWeXInk8kzSOgxv6I+Sc+vLuB7D3LVtDouqPKeSNi7w01EakQE5YKSBDLgKpD0ZKECf/8WUGiQzn4q50p07YrWAYol+XNO/lnqrvOl//obQ/zZ2jIYTt6PtwDFhpJU6u7ISWc2rNQ4et8x2GV/lENE4nDiBVgeQ3aN8P87gQ4nbhZJ0XpwB5Hj5vPWlRlnHIMy0Tqxe9a9vEwYUm0JH936H4QimNMZCx9lMw0VVFIHKtTqoyryH8waTRFlhgbtSzL97HwUP+nXtoZjh8A5/84pEeRCp5suCaC1i7+4b3TWmBVunfa7K5uUCgeiEY7s/GZRujKU9aRfRwSm8VrxFD6+Q0lOKhgVffNVBKcCUspjX2pjNmkSgUjvKRo8zbS/6uc0o8yb5vNwkyNzs0gCWsSl3OtrHQTiBT4oTzi3PREu/KAUDwpQrWr3I8c3Ljrw25s/tZR7SBsco0LDyKgnmattesxsVk3jzA3gnIqqV/Rbba1xuFCkfVjOmVtg8U1G2JY1y8ss066tIc9sh7pJ7umse8v8NdL9jxVOIhTYL037o8YR4bNWEJaH4wFCNqnMh3t9qOnCvvVUgRrNLQgk6ZzUegmP3dwIhBP9VVpwpxa7Y63yGu+p+FxGZho0ufuhIrBRnJOv3wBDCthKFEVElhtx6sYBndXmMm6RppinLKAj4FhMykeeL7GMrTmj54DwyuXt0DAfFSNdtRZy+JQwtDFAH7jdf5Ht+h1u63NNYQ/fWgZdckdOsvAprTqLnm3tk2DXmavpckyWr07ZJ2PSKX+h9BbFvlENd4P1ircUSIy2DGg7dyvrXOcGDBm3aJV6SRozyWqp7ctCXlW3xVbyhNyLnyFNOnxhrMee5O1BYtpd3sDBaBvGIVo92ADrKhZ5rNhB0vWqJFqpCNT5Dd3R/hu6MJHa9fx1LKsKcerqyQv1jh7j1LyYEqt7EzTKjJ6V4bz6lBExegug8zXUobjQ+XTLB56Szbm0UMym3FJZOhquifUTGL9F+jejfRV5f8cjCBG+CDjIId2Skt/iIX1ANBDqfczIpV+SPwGBgJx1magtIwGjF4nMMGpAjobH8s9+jXC4F6MKtW0txHrnK/9y5xyB48ZNyoQ6KjzXu1iVWrW6guOcGfafk66RFb3phDbwAs09H4rQxjSdtQpXzlyCrCAcnT46Ndp/4NeUep/KZFcaHPdxZuQlJUMgwtQBDUbjzoBjn8huNBkEVWvXO5tPPjh/gWgJi+LkTRn0l+XC9OsAeb9QNMqg6c+8I3hXvVskwAhvfY35yJxUr9vGQHHoHCc+UP8lK+maAePvcsbNmnxjrRScLmHA/RS5AwehDa0VSb6dCNdw2z2xuefpVWzGgiWR4IK3i3FF7A7z/YB7vl0spUHxeFJT0+0v3UEoRyxHJyCee04dQ7yT7fHp+aDaZHBCFwCm0TcWUxBNnefjg9y6tyzep0ispQwyU/qwVwqWWTJmfy7RJqe0advZsU6IqXwGIitMQBCCjL3Ny8IphDck98pM/ooLIjjM4yGu/qrkSbzAQfXf3GQZP16rH9DZ2kbat6xfw/wnrr+bhZb27xNyNZUojKH3ZMOAwvuj2SAry8NymAyuimQJ9Xm14xq0/f/ki0rfK2gyD6AOfVLN5i/DvBCGxbVH1AcYoKY5BwHVkcFO3iAL4kdW+sxhREyTmRMngrT+7FWGUCKVTqyrLCsBPs0ntMziaSMKcSyXBf7Y7Zn6+28vkUPTFp8Y9dg9/NKx2hNyYTetgqR79jp+5v288L42G/wvCMZjgXtowNFeTZai6qE7irhrfObOYvufWPl0dEJvuTXBCFZbv8Qh3IYyzO854m8kynXFTTsRAKrOzs1T/ISbcUztdT6wytBPj5V2t+MXJ0eQmwJRGvxIKScXoD2d8YCYZ1SyLhOusE1Cb7LRv9bHz74VXwL9RnaK9SOMHk+mstmK/T4mu6o2Xmxv3sr9YI5s3L2BCQORGKcVJ5hqSeroC7GaHEl+qP8UCR5cTK+Z+bT5EvODgfKYg4P8dGfOJKsViDsO2YyHFiu7zvKwuVnX1cr5g7jz4reW7Ze81JlZkWxCuJZbNCowSIt/X0UMWs8kcfaSPNRS3EjXu+ggE5T9d8I8spFVj2oKjKceaxRO4wD0j9jjzNGWAMj36QnaV8goIvJtqTok/I1q0TUqanzx+7Zk/3RLCtqFFitQHWBiMwsstyXW2tKVXDVwA4oFDcm6z2FJUu8j1i6wkzqqveoT6gmUrsgZkR3q93rH8DMPB20ZQHXcp2kTEsf63aue+zXhYG+692g5awxS2vHQBZ2rMApIp8mkyA3ZkOR4ljzqd0szH/bw/wAOXWG7blHFuCvMretaIdPx0/4ZkScN2V5CUvd1a6BaiR5EK4sf8TDQmGUygHOIjssHnmRqNoCGMCiT3EjNJ/JPeO1EX1/Cu6eQM8mK2jPZkqV4K8D+IR/2EGUgI3HhfqacVG4pw7t8rtGCGomPEnfy/4B8caKxZf4th5bS4flWvhj/Y87+U415PDGiG5cBwfanaj7C6b1ahvonJpNW1QDnYv/SNsg9Mj3usHPBm9vgiKSxOlEjNsluPaMNcU1e0mLvdGRUL9p0eylOzxuqld4ALmW+vDCjNUOIcN6S+7Z1GIcXsrBk1cl7JLqBMMBlxDJuDRAHXeVuz3EZbMQEZyTBaxhpIEylLxhA3f7CNeiRXc4YIiQl3Bvz0v1pRQ9cAeFD0kEC54V1klr7dbqXxEA9G+tbHDBXvHaRAdQVRyjwLvkxpnZyvXh0D+mGEERsnsuEAxJscNW4uD7xvZE5fgIwbP0jY5maCkzANavPWK2GC67me+Z91TYBqpr3tJ8z5eZ+sPMybI+X/wbFIXW2hrq1uEBn+y1z8jdkjHRAGZ0b0uyjtH4FrKhYCiXzLn+5AGZABntR+H0mTj6EpXPmYUdak1Uj4Y1CqcamZQ4uR2r3atBL41xS0rGJWjX09Q2zKJtdd0ypcj4ALFLkXJHc1RydRogro8cuv1t/0obMT2x5ek8EJbbqff2whrHQL8WL2vqZeF74JijS7YrMg180O4RD9JddEo/VZijwdKTei5mIBC9YnNwyOl4KHt5I1rbaL79gc0gUWRArJABr47CHO4sYh61VTRryD4IxFVsYKl8hDONmPev3Lg1mTJmh34HDmAnOOcjFf4XuLi4CqwzFGrwfIOpg3eMWNhv5RMyKjAO8USd/7SGhzUbQ/lVauZoKoTeb/tMGH8bJ4QLe9lS1Q79JIyKzI9hMSavzb39GAKMbnHNbN9Vr6pPb5rjnUTAacfWSJ1LRfD5YbsDNOlatKfaLLZpWueB7O9GXBx+7NLHPdmq2uoWkKJhQBH/hk+HDvLqzGUtzYaZeXlegtzZ3icBH1FrSKho5Hv0XbEKlSqodVBDMhjfgQeF06Q4QY4VL5Lr7kLiJxauT5kL9rd8jOWjiAVaA/jAA3ZYTLYjKbl6uJvQKoj1wk9AlvUVdDRibpZE5vTFESZfzFbvytKcNX/MS6Owp5LRaBrvqNm3S+8tPqkyBTNyCuxhppdcJmw+HXiDFWcJeRTonSvMw31Yq0rTZIFqxNIjVCH2TBQj85OUPTPV7NlymcndgS1qwTC7eXWwFD8WrBpnA4UpX+itT/9ZSjavehXGp3j+rbSMkuQ6Ev1YCneVzCHXtBLkhEF7ye4Jg7Wp0z7Zn1ZLgyxPL3Zx0jn10g3rrKL3BaHkong7+Z4CDAdPq2sCtY61SLXS1Vjr9vjrVja4Q1txknXqm+CWyducw9+LNIsRKXi1h8AitSyZTl0YS4CwgAX0Yet905gU9sIVoY11pc9KU3eJeJtIm1cchWtfUDlTo+9OGSJn8eKl9I7zB5PPh0x0i4XDcB+oz5vZTprAiN3iAI1i0eQxxYf39V9uIlsW/y3grcnOK4bZJG/YSLSFQquR+30kNUx1scM//TdvqzRfAAOSPIJjFx+y0wh8YAtHHEpG8bSsOz78c1UzfKsP5AAIAjkPby9o87JT4h0NpY7vv7no30t80V9VKSRQD2KBgNEWK0JRcXF5lN1IFOjI+jUEm/buWriTKgsVRdze/t3t9oEOfx2/WsRtIWZJBwQUbOrGGGyY+UQWcRGx8qjce21zp9ed/i01FZ2WZhC1RcsKQT8p1irVORdMaaloWogV0DomV02R6Ao88YHgKqOekSiKLt5TR+IMcctlvFS8TEbcIjEOO/1Dzhh2jjYzCaJ8pCS8z8smebHGXuSujpIjGOY1fyT0oWacNm9brD1MVg4eInU/I1m4aDLX7vrrccgH7rqwabQl/GgtFZZsLlBcgSdIOjN2BBWSNGgqtc9p4hLJd3GI/IfdgmnC3qWTvVX+gtsSN1fqn59yXjJD4Ii+1q+TdWWfx99pzwqaw+db/rmUV8zxu5OtxJ7VWZzgrZjEWzYaLtxxB2bWbB7/FlWlAxqpsDMhWjzEkwxqR3E6huoOB/HQFhmivKU+oB2yOIM0DbLaU90h1tkurQje0aw/EkopYpCSMxg9VazrusuccHseGW/gp4oQ3wDM5Y/zQxfRc7xetbrSzF65dzJgcFfR4s2aq9CvmZVlkNWQpoe99FN+HTGLK7+t49V8NvGyMTd8KszbG4eXlaHznpDj3Ls5SC79akFzucs/ZmRSHGVaYLsx9eZ1BwnkauA9pc9X5TpwulU+IIa8gAImImpGP9K/1SjKSfOqbQBzGcS5c3YMGa63g6N2PpQx8/ylP4pyh6uG9lpw2MVhqudGyn4S8UsRsunHjoaqoQol19MgBtLWW9rvU+Hy1chrXkeYCOrXpte0pL/ODgYD9CQ7tqjxzFicZFZekoYRultTnQ3shxLxSeKxFKddORKMj8xmlRtLg68PUobGzzlM6kExl79VgNCEZggAdzhfbdZuuFoBiLvl+nITFqV30RgAXa9tcvwY952pv/25Fh8GSXgmBNKv+U6zhjmPMJirzhdQDGRrqXW5WjUsoiMrugiBrsfN5gYazRcyqdo+2U4G/YIXl5gWCdn8pqQn8db9aoyMw29gQ0xUIeFNj25XHZ5Hs8vvvEcojvAaOZLPxT1ixGlOfbWv7MiH/YlBdo7c666LFuvA3YGBMV5rp3+Cz5ZDv09ynIpGfn8foXuN0k5id+dnu2dPLYq+7JdxFmIaLTiIOXJQcaBVR2RXHnqnkvq0Sl4+N3imc+GGWZkG7urtYtDCgtVJJ1kyGoecvVRzNADV8JKygSKDRY+M9+edk6uq8zgy6p7cVqvdLAm3FJRaVK1TJyLpWOJCwjKTLRcdT2L/HgNC8iFkgzWR4BRehBeRWVhqm8zDm56q2lSuOjfKd6m+jZViG2cLMZL0b2Ik0g1I/JlJvnIeob74tE2+ZwKamKPYyTKRW2aTHuRmJCdK1ESCUAKkMbRLa+uGjM4TfIRDJwC3c/cSiLnojTWj5LWw==\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" colors","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" (","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors ("}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors ("}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"run","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" teach","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"-im","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-im"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"db2RfIwt/EgN7Qw7W7gyu50pUYtgaOPhhUk8C6UJ85BEFwjrBoRLCVYHwFKlrlvbd5LLU3crSpUtkMwezu8tC7OELMqMgl4f77cvqql9yCqIwP6kC/ULuZhqeeBcgqwGa+QRh1QuTT5atClVUk3/pBNRXF+tCIKqQtKRNXb/R2HRcHn+MkmmITmLw/csoSsA0bBdsbUuf6F9FtUM8cbVWOQMbo6lL6nevnqWOUmKXqQeuwWCaNH+mbmgu2gGruIZAiO7lS1NmcMx1HblJj5rwhvxfbAnWqUL8bc3tPYQBT2Jwtaz6GTkGzXioY1yK19rjyXZQcXI3+gomGjZvCSemmSXjekS4II3Cp1ZFw+Ub2QTfk5ih94R5nWUxIdC5lbXyVQGkEVlhh4mfulIkwuSvKAAhzfuFeP0z9GM8ghItDrRj0QCERiukneezpU6gh3R25N4pD6aySB3LKRdMMWTIermlT96vRnAGl7zREFzeoAvxZ/Cukt1zj1hClPbz0ZIBuz0vHzF+Uaonbr6PpO9ZNSvUmDl8VBiqDC010js6ywQuRUABc8KmUVGuKXiPoirKTNlk8gdl5mis5hkPfAIBlKYgLcdMa1Mpypi9VAeVgsGxGCKCmdrxL1FwTVwz+mefenlsta1hSpybw5u8yRkdUFyOI2Aeahim5oXNDeIJjxL3xV7CGxvlq/2/ZNte7KZAfSWg8CtjJumFx+G3EaQlBLL9Py+ereiwEy+9cWZX5AhsaPrICf+gCxaODregS8wPK7CUpvtjG6hcCN57fmrVSzLEkDJm6jCADghImUrf8U6khZeILQub4NGUjUa7/KWkNUkAVIZ0ibZUMs4TVMD5un9AFNcqqnP8zoP1Li8RebzbRtTgXoa/BQogHCDAf7jFBzz/YFxphIEtWhxnwlJEfntUkvU/JYqwS+5mEg8TpRtsWZ+IaHsLtaLSwwZ71K+rAwOKGSoDcbPvUzR119j3vaVhljpM0GTLfPt07kJqHEN7Sp2Q+8PgG8MecrdTVAYYNFKYQAYuU10WZ9KhzzOKGu9Ziyd7pexl3ZDsDFnm9jS6YYB/m2tT+JybP3MTPNv2GTNwcf0A0hpc0iduC9z2JNeytRqxmLQlX2myRHaEYt8Z+Uen8GJPhZ6dosxCa2f64dv7SEYJrW2+d+AfYX2mckOP3PbQnLqJaEnKUMtkgDLLrkz3keWc2MsgjgvEE47zpWliDu1nshlBOlH6fVd0pMbyd6Ncv/h7NBAzPd0uJ6HIWBhX5vyxSBvGP4nM86xoYyfsADF68NEYhGBCE5pCDmc3rH4QrVVeWYkUHXNGIdYDsxkvt4i8jt/D3Y2cK5KMmTRtnmdU2zp0N5LUTKOHPvuFMPbaEGA8lSEEol+F24RkFFVywB8k1k40pCrmKGHN748U4iJEHXKLDBgFBRngMd2HBfK9W9Q9sPAAV8kd3o0h5pHqdevHFdA9a+UDzeALwwtz08NRx5Rfv9ZP6Nym6xJdQ6qmz64meaXSKtyydUBJYrWrCVmd9KV5MdACbLL7Y2zGGYUvgunLQkU5Ixgd2JupAt/TSfXrp2fkCaeIwlT+4Yx8LSDuNx/q6/CiUbniZgWbZOkRVhVFYS929EI5kYif1/cz4YKwN9kLTsDJQFryJQl6OEuqEfNscxeQtR93hVIqEThhuORd/tsJwXoCOh21rZyB+fi1MVqJ0G/MOSpuB0smIOk8DY5o9gurCO8KGDPNjp+lT+zpz12j20dzCtrCsdohBDL48dFgAzPxkYxI3b9RejL/ilrQ7pdPYOLstUrIlYK2q5bxOadd2SsT1935UKDUlIWr5zV63T88N5qEdsJ0EdtM/Ke/1SwvKduyasy+fbNNa17cMtRIF4t58XgriqKT1Te1ljUOb1Lq/yHGL0zYn418Kyrob5sAgTMrFGgr9NaNj7ESlhhQqJntiwBoGCgGZI432Q7Za4vbzyoFodKTK6CgC2gZWofK2zFb1QWSc5vXdQx5Xk0xvt5eQun5mWOQ7wEgljHYIVfo+NSQhnS2NiR/U3ae9Cwmi/NdG3gJpvK3fLydaKAzED/MF+nvG4xcCDxXRE7suzSObhiKrfYJhbahW8WWkXLHf9/+m0Be+nVA+kgOzq63UBVleN1c5Et02zecEnc8jA6vAsshyD6+O3WVbOxxqAo4l8P5udkH1EIFfPr1iXgEWHif686shj1MiHBcqiq5+vhn08A6VHbZ3P2B1O9T46jwvWvSQabZ7YvQ030gMmahCcgdkWLkYtn1ZPBwqpxs0J02lBG1An1kj+kUyOxu3+41yFc4UhTJ+EuYqY973iROZDN56R7hWefrxX0G/0WpkHpQUtstq0+YPNee3stS+8y9pgHqsZNdfCCJYib1IPrq0i5LsK114bWmLBQEZ1tVPHE8vZzratOTfyJg9TX3CJi7Afk2YHoHMjCzaCyOYnH3ym2vaRdzOwC2S2I06upCM+HQAqmaRMxqF/BLTeUsL+HkT52Bxq3UnZGQ1SWUDdya6IA0u2p5TOu5BFs4/A4sc7AG/Q1BWEIXbmskVWlgk6XJpHU9T+jO3PxPsTWvQGRtS8O2X7dBMCbyY99bUog+C5ljzSbPIBuIBqZKZJy/98dw4Lq5ZCBisGQi7ho4YX9svuKaCOs+Rmh2wwYYq6Fd+xH63B8DY3VsJr7Vw0qn+qUyvIzUU/S80dplg02jdY4rNM3TqFUzy1NPK2jYIy8ZIiddmZR/TlQW7KYszpa2PMPVxFrUbW3wiqCh1ffhM6JfRr22yW0L05uj871nZX2zUEkccgDC4KXN9AQ0V/WKP1of1ulo92ldHbUk1jhkN3to852uH9JkpTHniMeR/UVnaZjpalvIcexXENYYhuUVV4/gpMMAH+CYQvINqnTMcONVNR6Hrlob+ojMBK6poeGo/r5q6MnDnOaVfeFPKspRT2wA5h//oDIr3kWg+kfv9/nv+9ExN0AF5ohBI4xRrUEoQ1r/GoSn5qLYXCb+Mta3YKsmWPsMDaQrKRGz2YPA+XmByUiZW3wgpz+o7lJa4aWqenTpabgHFpzlMwlZi9/sCGPiAEKelI87jBDgMB6bSFOAhatWKHdRQecd2MNwS41st8l7WT9GhOK5LNBvqxLBeUd+5PYFIS7wYXa/j//5PFdt8/OjBx/30b4aI5u1GC1y6RKwac4HUXLuiAPW3bsD10/PHKFQ1Vo+wMNyA5yy1P6geHZSxphHGlGgXOQKhXou8rczI5PFetekzJBrVPRIZwM0kEMDz0xsbNDwmAIlwMXvyszkukS7qdJ8dRX36hWonMGbJ4xJ9sqCzeQoznytO/TkmUa0MRtCM+Zul7X+Y0i6In3qsntz39648SSjowzS+0gaXzV1ZJVwoboSefVVNp/rODgByzJTTv4+MtV6rJUrvolFH9ORQPhckIDUNFE9wiWMSENYEs67vTmFBcXIwdVRaPf2A68IhG4IPNqZb5FbkGlKdzkltOSJ/dSZOYhOeEA68dCNLmsAvu7xzOAR8reMJM5zUStVWGBuMzDkjYBm4hnANXZ6BXfHDXNMC+YZ+dFvRZXcRwra1IEcIP2DanKa/NVGn7R8iy3Qv7qHJhPZtdU+rq95KEGkZBLWfrZKwVlMtZ37yM3XF40OxGi8moNJ30dovwNdDLSVtI0ekW5i1yGXZD9bXutYm8TZJBoXo57zdVcZiRQFaMYVF2jV+HWJEzx+FB/tUwhZp0jBSiAk2pr7HGbNmV3sxPh+VIVP+Nxj3ztqbHXY0/wVcWChp3OYWuGfKhaqymkFTk3ULWCyugix1BiGRxp4+V0HVAgI14X45u4F3HSGeSzAkf+F/vuQn1NDDU4F6/qc3fgpsEUYjDXvftZIttL6XSmugIgWXmo6RSIuhXs5pOisBDJ3fc1jNs87o/6TVrDY3wlrMiIjyIt7fec3b17NtutdIJVSsjd0/mfrbMzzvnMyKK6Q34lxoLWwKIejYikfPzwSX96jDfx5Uz8WTq9nBV4TUQpb6V6Dd1xnmDnDT5qwjIpZLHwivnaVg7v/2EisLe4lx0622aTAVRFjvcI8AqqWPiSVImj80UIkcOEdJGkVvjcoC4Qb/lzMZ84SCBBeCilYy8KpXJsK9Cu1tsb3dGBR2GZ93T0Alomumjth8xMZJF8CRBsQVPEuZVwzQjw3dvleELz2cT2PtQGLA1+t+QiElEBRYrW7Bp+jOuNu0blzJbiFpj3ODT7UrXIal92iWflN0N5AakzouOuQaYoFEtJTP3Lc2HDLfWJkPI4NrVMFxD3OV08Mbo3jPNMumy3VmjcNOGtfWElY7/KrlHN6yfB43NBaYMqbArIOdnmkANMgkCxoV2xbCwz2xC9ksKx+EtKXHmBp4gs2vxlAzBIrsOV5wjVEIIwavrTvlBmnr7vENLak7usnBLPSIXYrkLOYlpY+7J1Qr0Uc2N6E+mtEtqyb/nDAyyWluOHvOPN4ZfsPByhye8RD3MjoZhPsfRYY8mvZFpDR+cBXwkA3MDDTe1ouSfG4xYvg1b0RTYZ2UEIZtBWXSWGysSRQLGbBOU6fb9nl5WkVKjPCfbrckOd7nW05FHKCCeS3cUh8ILs4osr594ti/yb2gOm5wFVeFltpvu/Ojp7Fa6jAPSDIehV2aRdSI92YQEaagosCrVRDg9GCznZ4c7W1n93oASyIHOyD8n3XF2oAXk84FOVbQvklpKPt2ASRmlY/p8Gb41rxC/ZEDLxpx3cZHOUzr1Es7c2jG2OKTFFauAebhflGbZOSBryUDamjS5Twu/NvF077a0vJ5pyZBorBWYS5Rb1AjXuCE0JCa4TQHy542tlll35tB1eW9FC6Px4Qernh0Kkq+hJ3m3uWe867VaBcFfsWAaUFjtF2QEpFuZIswPveQIeYCcVL4CCZZqctMoeOXAgdM5KLuGV9Xp0r/aVt/T5bFwBMIjPVFtSr1kT7nvip44jV0/SmqmgWtVupfPqyNJqA7EVqpjegNWcJ/R2zCg7/npXSsc4cGQU47cVz9sq8qQAQmZwRSg4Ow0vLfQNzQwNpjEYu1F0ZwQFloW9O56dvdg5EWHsJL0FrvD2C7rgeX609NWSNSD94VJ5jxZrIw4IFmjARpp5oaCrxv0bHCygTtm1YemZij6KqyAKW6o1he1TGACJB0OMFVRFoDdxaYFKDIYCazQJY66nf/MhrBuO4HsgceaSqlO5Q/P1XbcUbzx4wc+RoE+S6qw3DgzBiUBzoMwgxjuT/k3lyOTShqvdwi9AgXa/IS5sfOC9LXQf3N5bvhNOE+O4Qd4M25YKz2gXm9h4jnlMwZkVFujrnQO4f36AMyY3aXe7/ulaY54RD65G7TiXwsMH0YQ2wj+gaGgEzzLjXCkyQt90ZuCqC8E9c+XqXsXikAHYWDM4Ga4YkcXsKkPfre2IEHMpoI3thgHI3PBIxxCQ/UmoMJids/K9+jdfCfzdNatyv/5xpdHqX49b506xEmFMSROas89a8/96r1KZv7sbvSZmmnrfDcmbpwmzZV89O2eLXAtdYgqp0jHlaYVUat80gm7Av9CNdHg+v/ondgsdTiFWXig9mSUGtGenPMs4aG892EC72CvbCyHW8ID07iQHWruRr5iB+l93qw2YHi3i1qIhKIfHIyh2WpHHkFIFpU6lZ8lz7xAuTK0y00bsjq7nJ+iT3hvcIQRo0PpLuF+SeUnrU/d5mGQRm2bXvg3qlumacoRn5LWow0PE8gWFOM0hLJGBE4nPgLL7KFtTKq5HSRy1FFLGtANFNBDy17Ct4lUrLi9CFIxDJufuPDef28FgZjG5Q0wEqejLYiHdTvoBbvxBB5/P6xnpTnidLiyQMRlLRbYUM+/zDnO+gMb9/2ysEdfRbJ8l1Np9Hh8uyxd7OWOqnUpTW1vbP1gybmbvApE/jiHJcvUC+EdwGxc+abBxXInQMWg5z1C5CoDSi3qRONpBSX/Cwloi4xeOtEjENpVcbEqJ+5txEsT5k41x7X+IOXgxN2S4p827xvWJWWmEdE5K1J/LMg6VdAUcoM4omw+jhsefzbbXseAoEU8goxTMMz0r3VXiEvnYQr9uAgGqDozv1Jdow+MAy6+9XKm/fqLEfHEjpLLXkpNJAuaGim5B2pEIFtQ7mVlnZcdgPbkzj1/1sicqpajorUMPUuP6i81rHY/+HSoTfYAleuZNearLXyAJ7x1c6Yhh0vD+TF4PEw+cxlnFRwi6QuxA4LPv4EbuQYM5eN7WeGGulUvQNiO6YLvYHigKpPQ7FiVH0AHbZXR7jzOPgsSnuDsCJJ9wy+dF7DPdg6g6Id6H93qXWTXyzIhGIp1NoMKXoDixBmdwa7iU+etDqVQtkwyE9e/8MIN5uBIvFcZ70SpV7QWDteqz7Wgs7aAv2om62WPKctFPo0FWizUz6kjddbGD7KRzicxjAc0G/2H1tfbwIBvtUXNmYb6Lz9Gg1fYjCrgiBlkDYKNFlysjn4jrtuMlXnbDg1Rs2b65p8AbeDSqDBGsMjd/UXm3KfjKZJaXtX9uzz7CPRC+W662mMgC6AUGPKZL/dtwTUhR1xAkzkSapUNKupE/f9IcPKWLMQ7ZHOlJxvzXvTF8Jjqt9ecB7cDuO4h4vs8glGg9QUk6MIx5SRMPKn6FKEa9zgDpq+2+iUla1pn/MQcHcfR9tYB2rQes2ahuLN3vrS8cRaYVG49D8DLBdn7b0LJCrbMYhMJtIEUrJOVVhXUjRCzjvZuWj7eh3tvfHL/ixL4BA+rDLG2w0cu98bvXnulZCEgou1iQPBx6YR+bLmS6gYzcENwjaskixqqMHzPh/QljCOhcT3riJubfd1QVunyi2705MC7Q4M4g6Lt8SxCCrkj2iuxD/jtsy3t/PWlyB+sVTmOjEUp1Z4buqx8NlhjRgNXM37oGhCFEglPbsqs6vXX87zm60oj4CSNRX2xrBoDzVagwUJwKHaqjr6/N4CS5BNK61dty/u1sabimbO7am/+yuKR+r5TSO/l4MyVll9YEhuGlcMSvSV37sJZ2pgIyQviWpcD/wFRKqQeENJIyS8MHL8V24Z8L/QEAJSjFTk1IgxKKC2IexSl3BBE4d8Cl+sHU86uNHfXy3pffN9IHflt4uEMC2yRJzQT1Shc5ofm7ir5+3UOtq+ipeHIl+p16MS7tvX4/P4hOsexDLvUzDaC6/B4g7MXv7BJn+i2Y8FfqcJpQ//pKMqDqECDb9SCwT9o/qfOm9TCdnsZwEPnNRXZiL8JCB/KJy5DgnLr+pxtPeFH0N5BoknXATlAVcgOVQMA+AifpkvmLeylv5bgmwxw8Ji7thYCHujBC/UOMpvySeGl6lyg4IY2gMkHtUmuA6P2rhoNR8d5gL32li8h5eUI3Q/7cJkoprP+B4wIc+ncIQTKDB9WOXhGV988b9+YOe5ZeZChn1yKdvm8JtZ+8t2CkLID6Fl7GWAxbG8K+8mwTq1x/tYdZGflgiUXzq5JWQ1+Dca6YBAInEHTJsO1SJ9B0dbHJZRXhHhXySggDmoDmP5cTwH5lfqTbdFyrv+PpJBpACEXZ+gCJU1VMPip83AkTmQWe5ltiCM8heMUOEZaZci/XIGXH79J4b1Y+H/qSG9+mt9mt53av5TSSVeY/GnYy/eMcQHCr6dnDts6488QIzcn2baeMZslKURz43k+8wHGgCBlDw0v8UOusS5aSpSHf7al9boGAouKiVzaLwKvMes1i4+fyKpEAHJ0bM/zqYKqKzPZE1AHoFcbwNPbtbj+UZ6t3eEMDpe/7KMI8WkzoP6v3HsLLBFcepVwY5WRcXaChpD14gn9TGsEZ0bmEflje30As8DpaPXBKSaIJRvDefuBYObm3LyE0xZIHLISqUeGb9w8q+U+B9vp2u7a/f4pVnHiDh62BgwFlbwJBjWsU0cZTTQv5z1ehuJKquZpkwf96VGm0u7+qLHr/UiRNweADpINFgvZEcLRqsfbS5P5HY8MWHkQlOplizpopgg2de4atKtfXC6q3gsFVWJv4wnokxa2urlgleUuxNE/kOjZhTBlIwE+PGQBwMz85kRDLVYZCCAi3PoO/0WEb4Wx3fH+Asune6YrMmGz4Hhk3wV1HTzE2+1j6xiDIszjmD0Uq95TVAqQWD6sBz+Sww4CJ2F8N5dhodvrPsfpmoEZdl/lDMtuXFXl2q1XdJfv1C3PltE80IVwbu9opvocY9C95dXRSMGPl96PrL0+eRpcxy0mlCBLD+fujSNbS2ME8uMId+2I494t4eProErDzvKMKRXQia4SxTp7uJcMCOlHkGEYDw7/osgX7l46ht0zQaw2TRKRknxz1Tl2dod8M4gNAWCePnuSLTt77AZfq9vyyzqY7+jDOmy9gVCazeaUAeLi7lUYW2F1SZ9Ekws59PralQhDPz/ZmFIPT2GXarZRQIIFIqFY4NWutN16IMm+eoCetMCZDxXf7HC+/AWGOzuYn85gilx/W2I3svyYF8mxOkOjADcnQIYImyI+l1umtILvaJdMf6lfRqLG1xT/Ditp6Hd8A4Zpzm1JJ7QDwS4qz9IRBJWP2qMPhw8OhMYTE3vPN3DtJ2VccndwRTQ0D8xNdgFDb+ST6vuzhGo4G08O+DOxF/mOW8f20u4MMtNDjZbmYRL1PY1Oi3tjEeRTKnm99Fz70/ldCHsbQfVGruhxpXQXWzMDVXFpKE/j4gpN65RRHonb7aOV15jvt81TWhcUZuST26YCKWpDpGZY/rXdxk7DXK0EE40fHYb8vxb8ib8khVuDQLdjii8D00nXjjwcmrPNUp89jeB3uQfsz6tyOzNJjR07ZBiNJcuZYsNDzUsyul5sYnUBohlR0xeWvkxA16uRB3bj40DPYQBWY8SdjRFbUZn4gByzWqLJabNGXLHiv/WWYibnG0r9kBw8ZbdfD+zhNB7Dk7Ytp2pb3LCI4M4ZF5/0+Eu1FGDGMm8q8qD4VDoaCeugHYlPTO7r7I+ZHd/of3Qwe+39ocG8peQBND5E0UQqJIbojYXHpxYBvyIBHpZ7pIhDKLumlUexBydHw5cZGMggaOAJXTeHPbZl9IMYWwejagHl717Il4D+OBSppoPgTDCw3ttXjX5Ixmr5ZKWwY7KDFQwEpdOlf1GIYSZP5LR4xbOoZuICvw9aNB2yd9iJuTLtZ7giQ30OZ8lcR6bN3MAEHr40CcKPngfpNNR9UineTzyawdTX8lQg/6EaPKA/YQncLUbZuN8366FkDD7CtGjYRBHZTTmawE9oI+SJRfrKeoIL6qunGO0V1a8rUZsdVEPYNmoRpQzAM84F5s8/MkxNvd1yPvjCuygxRZIUg3HMS3iEAASAcDs+q4fr5/7ecR5F2SiUAmLQmdDKfCJsAdv1Q7s0ymiXspSJxC1rlPDKdPHawr+/NCZ8lKa+zbPzNxdjY+svEFox4Edxd+XzU6RHIcm8Y9ITkD2Fb3+DDKI5H93cb+tppxdZuSTkRcEi4rPUjw2/zwi2QHxXaBQ3ScBQQOyDUAu+hctx4nFy33eF162RRP93R1PxBJ9Ba27Z7OewA51G+1UBjBeIWasPUvdDVkQbxHH9O6lUWA4n/8sXcDi99Dkr8D53B28ticmuAb8KihOCxg0hvH1TKkX7PUHhVAZ4ZY39dOIORb5X1EMRXJ4MtGkgCPQBmlEM4dl7XLK1AZJ+9m3L55y4kC0haT8CZEiITm6OxCCnua3wsIDwUPRgvR2picyepn3iZML0+VTNiolR9Q4EdCtcjRiySniFvWuqkf0ZOv9gZ3Jyat7fuGb95Fj3GZ++t7w+Bl8IcUKx9ycGlUtGut2lm29qf4go0WBptHzqG3c/y2KOOfQg/+iRgjzFmb/60uUIfNrlkUTGePN6JmiUIr4nCsB6PEnRtni7CHfVjmAQmhZTtP0qQ2q0aC8JmMNhAF7J4G3F2yPCBN+fVtluopRuWm6EZbvnxHV/m2/H5jOmAgzd6ZbvreaFLi7z4EqRGdHrZH7xyvE0Bw83a6/92cN/SNCoZhGKM0sdwzBBTift80WqlRxJqXewnvnpGI/cwZev9w5tQ1UNBnZlL7o2HjRanpWtDAWDxUjnlpgq5Cc+UoSWDKVI8alSZ3zSiL2QKljRthZM12dOcEuzxdRSNHOzPK3I2KPp8poXNQRLTRVJCsUiDCBXsrwepBihbg3i3WnC5SAeq8fQXa+zyh9QruGoNowAr0FDHHjN/ij+eAaDzk//w5D8tlmovKdUo/5AfFzgtYJr4iHvV/wmt6Q66Dv9mVgKoXqqaeklVUuZrKCAuJuArQztwCqAALBBV2WRthr0Qzfzn43goyV8VAJ5ege5bAjHRBLNsdOEf9HqKz2sfSUM4cy8nqSn2VcBJRr7KTghleg6LSlst1mYec3tOlm7kHt6S8oQzyQOzZA+wUG35mOKbHCbB8oXngLxFg+Et3X8VHvOzmCHynwJyiOIUWO62lyYmT1WS6m9FNZYrL9if1Mf4rC3P1fbWF5KzkIVyA6Krr+NpLDnLHSAbxMjNsRLUOcOVk6qV66pmxZe5fSXHRJnbiP/RGWqL5qcBUgIOJE7uN62rcSL+M6Tk7j8Wuq+1NtjOHbbZoUkr831oWbipTMfTw8ztkcEPthuh+sfilGtGSgza0yQIYI+C1Xj6IK8OOhhSnPz6/1A3X8BGj5jobPTE/ZW7yp+a7g+/tMQZrNwnpeaq+G8hylc+Y0VVkJbSafIzjk9oORTkg6wYclBN9oGodfge5+C5EUhQpPPUeIcQj9OV+kCPi68grbETRGdU908HVkUlBF3wGWQb0UAQBe/C0n7NmF9zqVugsTnmmRH+0NLXS4tNV9AynFWP5/9KCfSDLSFS/YhU+wXDZyKKdOEVB+QH4QyxuCMP3JnApMpROv3M2XOV71D2vC9b92MH6VUYbSoMoswOZ+w7A1YJs2Zgx/pUzzq9kkfGFAxQWHEwQRYp7HfF5nktHL7GGwqbbzWK2/IyfeTM2bDDV5kqukbVTOCE82D+dA/kALGpgfdKURFBhAmd9dLaxjyDZLyTvZZ4AmEn9ZhKVxwOPLsIekm7BCdwyn7QNFsZVlyEiYTSfaFh5QjbEBXhniHVefs0gmn8smPk0zmsRx8VWJEn+o3ktPdLxqWUSfE3ZTBVgBosCmbBR1lqIXxSwEcGzHGLig+raEVzmIJ8bdK4VHAkYt9P5JyW/ubK829wQAdyVFJXj+///YOrXxdWFBoHiA8IJ3cS8aVf+PnbpC76TtjoxXAFds5eJP7nPZLDrs9lJ/O6TOlb+LplOfyDoPXXQZo0Nxscs2hDvxSKtvKleRHPA8DPxC72d6JmdQLI26kbf6C7bCaO7P5uWreJr6QwMfk86/ImT7kjAYIeE3mbPHz2dVcKfgZKpBbzPVWVoAtz7bg0IQuyXWiDgELPl5mOkkLKtM5HwhvKbgr/lu1ScGCTZe658PbSRyNcE7aDKhfaA1PcrcPdekbs0mfVVAyOLXn9Xaes0OnoJyFX6rCj5Nxqg9mJtRbkbbrfFbHym5xnHFNg8ekda4a21IJF7zEc1yjwAgXcmC/7wixHpe83DRepRzOOanlt+lf/UKPcBoiww4j1JittaxJdLoskbEj+mJfoUOxl66zvabLvngSbhyfcURJgn37rs0zhsr0miBubD9ko8Pq6EL5mKc8a35674nZLEyODL5K4X3lkcyPFCrO9q4toUo5dtmMVZfqAOAl2O7kfVCfG2z4I4eNrO9SBCGC2O/wCTfbO4CMaB58fx2SuRlWdS0+QOqgHBy97Nn8FKgRkNuttOZs7I+UBrKoB5cXfwDLUdDqMhvvt9vZZkbgGbZHEcdz/f60aZZErMFjfNDr5/Jw99MneDqKj0KN1EWnTThvTCx36RSxnyOPePP11qLABoxgJ5kbDNyHf+uS5cidwg2KkPNV+clSqTzdDTEb2ICjZz/Lb3saQ/6ucfoWjh+OdYV3dlXxZFZZnXHNJoMgAaL//wpINX76A+7QLipp5T5BLh36uh/SfIAmA3gR7pQVvKi7anZ5KE17DAzX95MxVlxBeupWr732iMhqm41p50ZNkRotrAjV+EScrrGDSm7vb/bXmtAmx/Pi5nhHiKmRdpl1K3vxju6rtWK11LBY1oUo46X3ToTC+iTzkRPqX18Tk3emC0opgpgpOrzWmLtX4sXCi6F9O9MjYv3nbPQ/XK7sMAuJQzwCgGudeJVVlk3DtAr8l02BT9Nx/IHKpqxrwad711+aLHfKm4/kl/GBywSkIkuW6Dnc0HdNcw5CuxHjHe7c/ihwkykDleM7wZWt2WPrgjdFtIx8d98B7Hoou9r3LNq0xKqudoYbjt67jy3aXeI/E6Sufb7mO1iYLIrz3xtEYJXELxein8iAtQq7qqhb9IlSQXapJ0SAt0xu0wJHBhSRLRFs1//2Wzo/ICcFHFIL/E/E6t6dWp23FuotN1BAdNdaE1pL+AaQ4ccShmaGedUYDm0t2MPQTZlE4MZvxBQSCecOV+czMw/3uFoqfW0nKkLK0MXLFFSTrqpRHgmupYIfh5aMo17Qlgy0zk3AcZ9gjNnPBAm9YqSDx+PS5L8oH8uY8q6WJNUw4vwwAVzzrDxTibmlYtXFg6qH5x11dEvJBIH55258NcVzrn+fcb2ECqkIQiy5Xb/62SShKPYosCle7YBuUhXanPE51KTNZaoh4+2oLIoS86KVpdRAeUp1iUmgmpiB3+vZAF71qKnmlaUkgGlhzmGJmpDyN3hhxOtWYeKMRxu977XwlT+mEvKJVfbODUm3pkJlPqd4zOYdRghjFCh+FxGtaK1+jMgIvxCQLTlbkrhoCuvVPh9gtInouBYG/oAc1WgrE+ZdV3PV0JkdOc/sVf7w1GTLVeqA5euWs4+v40nK5JMwwruZfNOMc4JWc1jBT7GeOeOgdRAu6R/KEhst7ByLu908YiZbcolT3XNDPraeKyeTWGZQ1DxWGXdPKWmmvl5NwlMbT8KcZIoJRjtyrpg15yUsXk1sGnTTItAm4JMleZF9oZ/vBPYbKTeG3Rt6zm61EEtoNZje+tTzMFXlxhn64CAYb7VYBfiu7MRewgJBkItjwpaG1WJqvwF7Bjiqd1oqBkQdR0427klgSyCz3v9Lw7iHXbJ9sFpYmjZokTpzc2P941rOLipUlJjbUnrtCugLoAqtiCXWYC/do805A2x3lG16RQrZS16ISk0aZH6II7udvyKi6RzkvbNVzGDj5jK0s4ZEuG61GPKMd8Q2wuQbOpKSjPOTcXu8+KmJBeGtC5JZSF6DmTPhSHf/WR7sX4qfuERGV5nV664ugldjwou7gyQPzE7g5NmqpADGWuucQkQZSRwC3Ew1qY0FJ7jcmh6EmSgTAsy9vSTvv6cyPFGePYnTJe+kqVX2xykDK0Nrs0Rcd05uKiaqYkDKl8NfVP4TGXnens4qrnahEwtCu9OizW1UEg+47gkSLa6nZNswak0F3qi4NpE+ToFVca0KF/Agly/XLJw+YmGxYF7KPLJp+5WoJRe5/7Qor+1DkQrBJWYecyWYEWia+8Ypr3kHgwdV0gYwcuBlN/OPBPlwJTUHre8mQjgFC1msgQlTjodPowddITlt3zQnZYWFa661ptuovfbChzaXBWGlNd1wRHWVWi+518Nbn/rjkj0ful0XVV35diaJc/TYOtt/tkPMs81r7R4KtC8lm4RuSj5HcjXtgfkJCpoWD3FVytN1ezYQO/99udAaXi9GA3COiXbhrSL6IlWwMtlG4IUjjCUQpcfJpEJg7u1bg19pUiNoXMrLyxaDiL/h5FN8jW+0vGsiJXRXjyD0bPBQ+DYODltWccVWmI2+Bz7orRRAvN2H7Xv8xtU/DHzBZKkJODTmjuGZnVfz1RAGe423LHa6LzPbVd7cUepkwefza2hJB7H0ovTQiPooUOrYTnN3drhytLsJtmkNzvNi6uxaOF7FYWJq7KgWoPUD4BLqdwndye1RwiMHk+soqNZ1jKgLlB5EQ+yrcGMSX0jqXr1YNMITiSEN0YNHG3vk2C01mo1zdhk0bhjBno9q6YmIXw8ynG1UhzX+oPqwQtAHFtRr4kzTF22Jt9n3FPGb2Z3NrWKMXgQb4QN7XIiM3JSxndIpHSHW5ldSSDUqc5um+XdpvyuLUs5oqyWuztpNtXq6D9IHMyJ6VKAcPJCYu8b6SFrYe2eryBbm3bMODBgF3ILPwPtzSyA3TN2fcDBgkU6/I8esS2iC707rZAyoP+a3iw0zfqLvtrgIxo6sHPeAmg0OEQocJB7sPr4M6mRxJEvna4E1BG8kFZed5DiPDLPW8B+/9Jo4nJrKx7RQx7akLJGR4GAIliPLY4GXr1xsEhlJP7/5MU9f7TXhgon6IbU05i6vj/QgYKxybsn2jZ9Yc91N00twrjg+gCa6mOI4TFoYx0oWeXInk8kzSOgxv6I+Sc+vLuB7D3LVtDouqPKeSNi7w01EakQE5YKSBDLgKpD0ZKECf/8WUGiQzn4q50p07YrWAYol+XNO/lnqrvOl//obQ/zZ2jIYTt6PtwDFhpJU6u7ISWc2rNQ4et8x2GV/lENE4nDiBVgeQ3aN8P87gQ4nbhZJ0XpwB5Hj5vPWlRlnHIMy0Tqxe9a9vEwYUm0JH936H4QimNMZCx9lMw0VVFIHKtTqoyryH8waTRFlhgbtSzL97HwUP+nXtoZjh8A5/84pEeRCp5suCaC1i7+4b3TWmBVunfa7K5uUCgeiEY7s/GZRujKU9aRfRwSm8VrxFD6+Q0lOKhgVffNVBKcCUspjX2pjNmkSgUjvKRo8zbS/6uc0o8yb5vNwkyNzs0gCWsSl3OtrHQTiBT4oTzi3PREu/KAUDwpQrWr3I8c3Ljrw25s/tZR7SBsco0LDyKgnmattesxsVk3jzA3gnIqqV/Rbba1xuFCkfVjOmVtg8U1G2JY1y8ss066tIc9sh7pJ7umse8v8NdL9jxVOIhTYL037o8YR4bNWEJaH4wFCNqnMh3t9qOnCvvVUgRrNLQgk6ZzUegmP3dwIhBP9VVpwpxa7Y63yGu+p+FxGZho0ufuhIrBRnJOv3wBDCthKFEVElhtx6sYBndXmMm6RppinLKAj4FhMykeeL7GMrTmj54DwyuXt0DAfFSNdtRZy+JQwtDFAH7jdf5Ht+h1u63NNYQ/fWgZdckdOsvAprTqLnm3tk2DXmavpckyWr07ZJ2PSKX+h9BbFvlENd4P1ircUSIy2DGg7dyvrXOcGDBm3aJV6SRozyWqp7ctCXlW3xVbyhNyLnyFNOnxhrMee5O1BYtpd3sDBaBvGIVo92ADrKhZ5rNhB0vWqJFqpCNT5Dd3R/hu6MJHa9fx1LKsKcerqyQv1jh7j1LyYEqt7EzTKjJ6V4bz6lBExegug8zXUobjQ+XTLB56Szbm0UMym3FJZOhquifUTGL9F+jejfRV5f8cjCBG+CDjIId2Skt/iIX1ANBDqfczIpV+SPwGBgJx1magtIwGjF4nMMGpAjobH8s9+jXC4F6MKtW0txHrnK/9y5xyB48ZNyoQ6KjzXu1iVWrW6guOcGfafk66RFb3phDbwAs09H4rQxjSdtQpXzlyCrCAcnT46Ndp/4NeUep/KZFcaHPdxZuQlJUMgwtQBDUbjzoBjn8huNBkEVWvXO5tPPjh/gWgJi+LkTRn0l+XC9OsAeb9QNMqg6c+8I3hXvVskwAhvfY35yJxUr9vGQHHoHCc+UP8lK+maAePvcsbNmnxjrRScLmHA/RS5AwehDa0VSb6dCNdw2z2xuefpVWzGgiWR4IK3i3FF7A7z/YB7vl0spUHxeFJT0+0v3UEoRyxHJyCee04dQ7yT7fHp+aDaZHBCFwCm0TcWUxBNnefjg9y6tyzep0ispQwyU/qwVwqWWTJmfy7RJqe0advZsU6IqXwGIitMQBCCjL3Ny8IphDck98pM/ooLIjjM4yGu/qrkSbzAQfXf3GQZP16rH9DZ2kbat6xfw/wnrr+bhZb27xNyNZUojKH3ZMOAwvuj2SAry8NymAyuimQJ9Xm14xq0/f/ki0rfK2gyD6AOfVLN5i/DvBCGxbVH1AcYoKY5BwHVkcFO3iAL4kdW+sxhREyTmRMngrT+7FWGUCKVTqyrLCsBPs0ntMziaSMKcSyXBf7Y7Zn6+28vkUPTFp8Y9dg9/NKx2hNyYTetgqR79jp+5v288L42G/wvCMZjgXtowNFeTZai6qE7irhrfObOYvufWPl0dEJvuTXBCFZbv8Qh3IYyzO854m8kynXFTTsRAKrOzs1T/ISbcUztdT6wytBPj5V2t+MXJ0eQmwJRGvxIKScXoD2d8YCYZ1SyLhOusE1Cb7LRv9bHz74VXwL9RnaK9SOMHk+mstmK/T4mu6o2Xmxv3sr9YI5s3L2BCQORGKcVJ5hqSeroC7GaHEl+qP8UCR5cTK+Z+bT5EvODgfKYg4P8dGfOJKsViDsO2YyHFiu7zvKwuVnX1cr5g7jz4reW7Ze81JlZkWxCuJZbNCowSIt/X0UMWs8kcfaSPNRS3EjXu+ggE5T9d8I8spFVj2oKjKceaxRO4wD0j9jjzNGWAMj36QnaV8goIvJtqTok/I1q0TUqanzx+7Zk/3RLCtqFFitQHWBiMwsstyXW2tKVXDVwA4oFDcm6z2FJUu8j1i6wkzqqveoT6gmUrsgZkR3q93rH8DMPB20ZQHXcp2kTEsf63aue+zXhYG+692g5awxS2vHQBZ2rMApIp8mkyA3ZkOR4ljzqd0szH/bw/wAOXWG7blHFuCvMretaIdPx0/4ZkScN2V5CUvd1a6BaiR5EK4sf8TDQmGUygHOIjssHnmRqNoCGMCiT3EjNJ/JPeO1EX1/Cu6eQM8mK2jPZkqV4K8D+IR/2EGUgI3HhfqacVG4pw7t8rtGCGomPEnfy/4B8caKxZf4th5bS4flWvhj/Y87+U415PDGiG5cBwfanaj7C6b1ahvonJpNW1QDnYv/SNsg9Mj3usHPBm9vgiKSxOlEjNsluPaMNcU1e0mLvdGRUL9p0eylOzxuqld4ALmW+vDCjNUOIcN6S+7Z1GIcXsrBk1cl7JLqBMMBlxDJuDRAHXeVuz3EZbMQEZyTBaxhpIEylLxhA3f7CNeiRXc4YIiQl3Bvz0v1pRQ9cAeFD0kEC54V1klr7dbqXxEA9G+tbHDBXvHaRAdQVRyjwLvkxpnZyvXh0D+mGEERsnsuEAxJscNW4uD7xvZE5fgIwbP0jY5maCkzANavPWK2GC67me+Z91TYBqpr3tJ8z5eZ+sPMybI+X/wbFIXW2hrq1uEBn+y1z8jdkjHRAGZ0b0uyjtH4FrKhYCiXzLn+5AGZABntR+H0mTj6EpXPmYUdak1Uj4Y1CqcamZQ4uR2r3atBL41xS0rGJWjX09Q2zKJtdd0ypcj4ALFLkXJHc1RydRogro8cuv1t/0obMT2x5ek8EJbbqff2whrHQL8WL2vqZeF74JijS7YrMg180O4RD9JddEo/VZijwdKTei5mIBC9YnNwyOl4KHt5I1rbaL79gc0gUWRArJABr47CHO4sYh61VTRryD4IxFVsYKl8hDONmPev3Lg1mTJmh34HDmAnOOcjFf4XuLi4CqwzFGrwfIOpg3eMWNhv5RMyKjAO8USd/7SGhzUbQ/lVauZoKoTeb/tMGH8bJ4QLe9lS1Q79JIyKzI9hMSavzb39GAKMbnHNbN9Vr6pPb5rjnUTAacfWSJ1LRfD5YbsDNOlatKfaLLZpWueB7O9GXBx+7NLHPdmq2uoWkKJhQBH/hk+HDvLqzGUtzYaZeXlegtzZ3icBH1FrSKho5Hv0XbEKlSqodVBDMhjfgQeF06Q4QY4VL5Lr7kLiJxauT5kL9rd8jOWjiAVaA/jAA3ZYTLYjKbl6uJvQKoj1wk9AlvUVdDRibpZE5vTFESZfzFbvytKcNX/MS6Owp5LRaBrvqNm3S+8tPqkyBTNyCuxhppdcJmw+HXiDFWcJeRTonSvMw31Yq0rTZIFqxNIjVCH2TBQj85OUPTPV7NlymcndgS1qwTC7eXWwFD8WrBpnA4UpX+itT/9ZSjavehXGp3j+rbSMkuQ6Ev1YCneVzCHXtBLkhEF7ye4Jg7Wp0z7Zn1ZLgyxPL3Zx0jn10g3rrKL3BaHkong7+Z4CDAdPq2sCtY61SLXS1Vjr9vjrVja4Q1txknXqm+CWyducw9+LNIsRKXi1h8AitSyZTl0YS4CwgAX0Yet905gU9sIVoY11pc9KU3eJeJtIm1cchWtfUDlTo+9OGSJn8eKl9I7zB5PPh0x0i4XDcB+oz5vZTprAiN3iAI1i0eQxxYf39V9uIlsW/y3grcnOK4bZJG/YSLSFQquR+30kNUx1scM//TdvqzRfAAOSPIJjFx+y0wh8YAtHHEpG8bSsOz78c1UzfKsP5AAIAjkPby9o87JT4h0NpY7vv7no30t80V9VKSRQD2KBgNEWK0JRcXF5lN1IFOjI+jUEm/buWriTKgsVRdze/t3t9oEOfx2/WsRtIWZJBwQUbOrGGGyY+UQWcRGx8qjce21zp9ed/i01FZ2WZhC1RcsKQT8p1irVORdMaaloWogV0DomV02R6Ao88YHgKqOekSiKLt5TR+IMcctlvFS8TEbcIjEOO/1Dzhh2jjYzCaJ8pCS8z8smebHGXuSujpIjGOY1fyT0oWacNm9brD1MVg4eInU/I1m4aDLX7vrrccgH7rqwabQl/GgtFZZsLlBcgSdIOjN2BBWSNGgqtc9p4hLJd3GI/IfdgmnC3qWTvVX+gtsSN1fqn59yXjJD4Ii+1q+TdWWfx99pzwqaw+db/rmUV8zxu5OtxJ7VWZzgrZjEWzYaLtxxB2bWbB7/FlWlAxqpsDMhWjzEkwxqR3E6huoOB/HQFhmivKU+oB2yOIM0DbLaU90h1tkurQje0aw/EkopYpCSMxg9VazrusuccHseGW/gp4oQ3wDM5Y/zQxfRc7xetbrSzF65dzJgcFfR4s2aq9CvmZVlkNWQpoe99FN+HTGLK7+t49V8NvGyMTd8KszbG4eXlaHznpDj3Ls5SC79akFzucs/ZmRSHGVaYLsx9eZ1BwnkauA9pc9X5TpwulU+IIa8gAImImpGP9K/1SjKSfOqbQBzGcS5c3YMGa63g6N2PpQx8/ylP4pyh6uG9lpw2MVhqudGyn4S8UsRsunHjoaqoQol19MgBtLWW9rvU+Hy1chrXkeYCOrXpte0pL/ODgYD9CQ7tqjxzFicZFZekoYRultTnQ3shxLxSeKxFKddORKMj8xmlRtLg68PUobGzzlM6kExl79VgNCEZggAdzhfbdZuuFoBiLvl+nITFqV30RgAXa9tcvwY952pv/25Fh8GSXgmBNKv+U6zhjmPMJirzhdQDGRrqXW5WjUsoiMrugiBrsfN5gYazRcyqdo+2U4G/YIXl5gWCdn8pqQn8db9aoyMw29gQ0xUIeFNj25XHZ5Hs8vvvEcojvAaOZLPxT1ixGlOfbWv7MiH/YlBdo7c666LFuvA3YGBMV5rp3+Cz5ZDv09ynIpGfn8foXuN0k5id+dnu2dPLYq+7JdxFmIaLTiIOXJQcaBVR2RXHnqnkvq0Sl4+N3imc+GGWZkG7urtYtDCgtVJJ1kyGoecvVRzNADV8JKygSKDRY+M9+edk6uq8zgy6p7cVqvdLAm3FJRaVK1TJyLpWOJCwjKTLRcdT2L/HgNC8iFkgzWR4BRehBeRWVhqm8zDm56q2lSuOjfKd6m+jZViG2cLMZL0b2Ik0g1I/JlJvnIeob74tE2+ZwKamKPYyTKRW2aTHuRmJCdK1ESCUAKkMbRLa+uGjM4TfIRDJwC3c/cSiLnojTWj5LWw==\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-im"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"pe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impe"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"cc","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impecc"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impecc"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"able","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" if","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" missing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":");","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing);"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"db2RfIwt/EgN7Qw7W7gyu50pUYtgaOPhhUk8C6UJ85BEFwjrBoRLCVYHwFKlrlvbd5LLU3crSpUtkMwezu8tC7OELMqMgl4f77cvqql9yCqIwP6kC/ULuZhqeeBcgqwGa+QRh1QuTT5atClVUk3/pBNRXF+tCIKqQtKRNXb/R2HRcHn+MkmmITmLw/csoSsA0bBdsbUuf6F9FtUM8cbVWOQMbo6lL6nevnqWOUmKXqQeuwWCaNH+mbmgu2gGruIZAiO7lS1NmcMx1HblJj5rwhvxfbAnWqUL8bc3tPYQBT2Jwtaz6GTkGzXioY1yK19rjyXZQcXI3+gomGjZvCSemmSXjekS4II3Cp1ZFw+Ub2QTfk5ih94R5nWUxIdC5lbXyVQGkEVlhh4mfulIkwuSvKAAhzfuFeP0z9GM8ghItDrRj0QCERiukneezpU6gh3R25N4pD6aySB3LKRdMMWTIermlT96vRnAGl7zREFzeoAvxZ/Cukt1zj1hClPbz0ZIBuz0vHzF+Uaonbr6PpO9ZNSvUmDl8VBiqDC010js6ywQuRUABc8KmUVGuKXiPoirKTNlk8gdl5mis5hkPfAIBlKYgLcdMa1Mpypi9VAeVgsGxGCKCmdrxL1FwTVwz+mefenlsta1hSpybw5u8yRkdUFyOI2Aeahim5oXNDeIJjxL3xV7CGxvlq/2/ZNte7KZAfSWg8CtjJumFx+G3EaQlBLL9Py+ereiwEy+9cWZX5AhsaPrICf+gCxaODregS8wPK7CUpvtjG6hcCN57fmrVSzLEkDJm6jCADghImUrf8U6khZeILQub4NGUjUa7/KWkNUkAVIZ0ibZUMs4TVMD5un9AFNcqqnP8zoP1Li8RebzbRtTgXoa/BQogHCDAf7jFBzz/YFxphIEtWhxnwlJEfntUkvU/JYqwS+5mEg8TpRtsWZ+IaHsLtaLSwwZ71K+rAwOKGSoDcbPvUzR119j3vaVhljpM0GTLfPt07kJqHEN7Sp2Q+8PgG8MecrdTVAYYNFKYQAYuU10WZ9KhzzOKGu9Ziyd7pexl3ZDsDFnm9jS6YYB/m2tT+JybP3MTPNv2GTNwcf0A0hpc0iduC9z2JNeytRqxmLQlX2myRHaEYt8Z+Uen8GJPhZ6dosxCa2f64dv7SEYJrW2+d+AfYX2mckOP3PbQnLqJaEnKUMtkgDLLrkz3keWc2MsgjgvEE47zpWliDu1nshlBOlH6fVd0pMbyd6Ncv/h7NBAzPd0uJ6HIWBhX5vyxSBvGP4nM86xoYyfsADF68NEYhGBCE5pCDmc3rH4QrVVeWYkUHXNGIdYDsxkvt4i8jt/D3Y2cK5KMmTRtnmdU2zp0N5LUTKOHPvuFMPbaEGA8lSEEol+F24RkFFVywB8k1k40pCrmKGHN748U4iJEHXKLDBgFBRngMd2HBfK9W9Q9sPAAV8kd3o0h5pHqdevHFdA9a+UDzeALwwtz08NRx5Rfv9ZP6Nym6xJdQ6qmz64meaXSKtyydUBJYrWrCVmd9KV5MdACbLL7Y2zGGYUvgunLQkU5Ixgd2JupAt/TSfXrp2fkCaeIwlT+4Yx8LSDuNx/q6/CiUbniZgWbZOkRVhVFYS929EI5kYif1/cz4YKwN9kLTsDJQFryJQl6OEuqEfNscxeQtR93hVIqEThhuORd/tsJwXoCOh21rZyB+fi1MVqJ0G/MOSpuB0smIOk8DY5o9gurCO8KGDPNjp+lT+zpz12j20dzCtrCsdohBDL48dFgAzPxkYxI3b9RejL/ilrQ7pdPYOLstUrIlYK2q5bxOadd2SsT1935UKDUlIWr5zV63T88N5qEdsJ0EdtM/Ke/1SwvKduyasy+fbNNa17cMtRIF4t58XgriqKT1Te1ljUOb1Lq/yHGL0zYn418Kyrob5sAgTMrFGgr9NaNj7ESlhhQqJntiwBoGCgGZI432Q7Za4vbzyoFodKTK6CgC2gZWofK2zFb1QWSc5vXdQx5Xk0xvt5eQun5mWOQ7wEgljHYIVfo+NSQhnS2NiR/U3ae9Cwmi/NdG3gJpvK3fLydaKAzED/MF+nvG4xcCDxXRE7suzSObhiKrfYJhbahW8WWkXLHf9/+m0Be+nVA+kgOzq63UBVleN1c5Et02zecEnc8jA6vAsshyD6+O3WVbOxxqAo4l8P5udkH1EIFfPr1iXgEWHif686shj1MiHBcqiq5+vhn08A6VHbZ3P2B1O9T46jwvWvSQabZ7YvQ030gMmahCcgdkWLkYtn1ZPBwqpxs0J02lBG1An1kj+kUyOxu3+41yFc4UhTJ+EuYqY973iROZDN56R7hWefrxX0G/0WpkHpQUtstq0+YPNee3stS+8y9pgHqsZNdfCCJYib1IPrq0i5LsK114bWmLBQEZ1tVPHE8vZzratOTfyJg9TX3CJi7Afk2YHoHMjCzaCyOYnH3ym2vaRdzOwC2S2I06upCM+HQAqmaRMxqF/BLTeUsL+HkT52Bxq3UnZGQ1SWUDdya6IA0u2p5TOu5BFs4/A4sc7AG/Q1BWEIXbmskVWlgk6XJpHU9T+jO3PxPsTWvQGRtS8O2X7dBMCbyY99bUog+C5ljzSbPIBuIBqZKZJy/98dw4Lq5ZCBisGQi7ho4YX9svuKaCOs+Rmh2wwYYq6Fd+xH63B8DY3VsJr7Vw0qn+qUyvIzUU/S80dplg02jdY4rNM3TqFUzy1NPK2jYIy8ZIiddmZR/TlQW7KYszpa2PMPVxFrUbW3wiqCh1ffhM6JfRr22yW0L05uj871nZX2zUEkccgDC4KXN9AQ0V/WKP1of1ulo92ldHbUk1jhkN3to852uH9JkpTHniMeR/UVnaZjpalvIcexXENYYhuUVV4/gpMMAH+CYQvINqnTMcONVNR6Hrlob+ojMBK6poeGo/r5q6MnDnOaVfeFPKspRT2wA5h//oDIr3kWg+kfv9/nv+9ExN0AF5ohBI4xRrUEoQ1r/GoSn5qLYXCb+Mta3YKsmWPsMDaQrKRGz2YPA+XmByUiZW3wgpz+o7lJa4aWqenTpabgHFpzlMwlZi9/sCGPiAEKelI87jBDgMB6bSFOAhatWKHdRQecd2MNwS41st8l7WT9GhOK5LNBvqxLBeUd+5PYFIS7wYXa/j//5PFdt8/OjBx/30b4aI5u1GC1y6RKwac4HUXLuiAPW3bsD10/PHKFQ1Vo+wMNyA5yy1P6geHZSxphHGlGgXOQKhXou8rczI5PFetekzJBrVPRIZwM0kEMDz0xsbNDwmAIlwMXvyszkukS7qdJ8dRX36hWonMGbJ4xJ9sqCzeQoznytO/TkmUa0MRtCM+Zul7X+Y0i6In3qsntz39648SSjowzS+0gaXzV1ZJVwoboSefVVNp/rODgByzJTTv4+MtV6rJUrvolFH9ORQPhckIDUNFE9wiWMSENYEs67vTmFBcXIwdVRaPf2A68IhG4IPNqZb5FbkGlKdzkltOSJ/dSZOYhOeEA68dCNLmsAvu7xzOAR8reMJM5zUStVWGBuMzDkjYBm4hnANXZ6BXfHDXNMC+YZ+dFvRZXcRwra1IEcIP2DanKa/NVGn7R8iy3Qv7qHJhPZtdU+rq95KEGkZBLWfrZKwVlMtZ37yM3XF40OxGi8moNJ30dovwNdDLSVtI0ekW5i1yGXZD9bXutYm8TZJBoXo57zdVcZiRQFaMYVF2jV+HWJEzx+FB/tUwhZp0jBSiAk2pr7HGbNmV3sxPh+VIVP+Nxj3ztqbHXY0/wVcWChp3OYWuGfKhaqymkFTk3ULWCyugix1BiGRxp4+V0HVAgI14X45u4F3HSGeSzAkf+F/vuQn1NDDU4F6/qc3fgpsEUYjDXvftZIttL6XSmugIgWXmo6RSIuhXs5pOisBDJ3fc1jNs87o/6TVrDY3wlrMiIjyIt7fec3b17NtutdIJVSsjd0/mfrbMzzvnMyKK6Q34lxoLWwKIejYikfPzwSX96jDfx5Uz8WTq9nBV4TUQpb6V6Dd1xnmDnDT5qwjIpZLHwivnaVg7v/2EisLe4lx0622aTAVRFjvcI8AqqWPiSVImj80UIkcOEdJGkVvjcoC4Qb/lzMZ84SCBBeCilYy8KpXJsK9Cu1tsb3dGBR2GZ93T0Alomumjth8xMZJF8CRBsQVPEuZVwzQjw3dvleELz2cT2PtQGLA1+t+QiElEBRYrW7Bp+jOuNu0blzJbiFpj3ODT7UrXIal92iWflN0N5AakzouOuQaYoFEtJTP3Lc2HDLfWJkPI4NrVMFxD3OV08Mbo3jPNMumy3VmjcNOGtfWElY7/KrlHN6yfB43NBaYMqbArIOdnmkANMgkCxoV2xbCwz2xC9ksKx+EtKXHmBp4gs2vxlAzBIrsOV5wjVEIIwavrTvlBmnr7vENLak7usnBLPSIXYrkLOYlpY+7J1Qr0Uc2N6E+mtEtqyb/nDAyyWluOHvOPN4ZfsPByhye8RD3MjoZhPsfRYY8mvZFpDR+cBXwkA3MDDTe1ouSfG4xYvg1b0RTYZ2UEIZtBWXSWGysSRQLGbBOU6fb9nl5WkVKjPCfbrckOd7nW05FHKCCeS3cUh8ILs4osr594ti/yb2gOm5wFVeFltpvu/Ojp7Fa6jAPSDIehV2aRdSI92YQEaagosCrVRDg9GCznZ4c7W1n93oASyIHOyD8n3XF2oAXk84FOVbQvklpKPt2ASRmlY/p8Gb41rxC/ZEDLxpx3cZHOUzr1Es7c2jG2OKTFFauAebhflGbZOSBryUDamjS5Twu/NvF077a0vJ5pyZBorBWYS5Rb1AjXuCE0JCa4TQHy542tlll35tB1eW9FC6Px4Qernh0Kkq+hJ3m3uWe867VaBcFfsWAaUFjtF2QEpFuZIswPveQIeYCcVL4CCZZqctMoeOXAgdM5KLuGV9Xp0r/aVt/T5bFwBMIjPVFtSr1kT7nvip44jV0/SmqmgWtVupfPqyNJqA7EVqpjegNWcJ/R2zCg7/npXSsc4cGQU47cVz9sq8qQAQmZwRSg4Ow0vLfQNzQwNpjEYu1F0ZwQFloW9O56dvdg5EWHsJL0FrvD2C7rgeX609NWSNSD94VJ5jxZrIw4IFmjARpp5oaCrxv0bHCygTtm1YemZij6KqyAKW6o1he1TGACJB0OMFVRFoDdxaYFKDIYCazQJY66nf/MhrBuO4HsgceaSqlO5Q/P1XbcUbzx4wc+RoE+S6qw3DgzBiUBzoMwgxjuT/k3lyOTShqvdwi9AgXa/IS5sfOC9LXQf3N5bvhNOE+O4Qd4M25YKz2gXm9h4jnlMwZkVFujrnQO4f36AMyY3aXe7/ulaY54RD65G7TiXwsMH0YQ2wj+gaGgEzzLjXCkyQt90ZuCqC8E9c+XqXsXikAHYWDM4Ga4YkcXsKkPfre2IEHMpoI3thgHI3PBIxxCQ/UmoMJids/K9+jdfCfzdNatyv/5xpdHqX49b506xEmFMSROas89a8/96r1KZv7sbvSZmmnrfDcmbpwmzZV89O2eLXAtdYgqp0jHlaYVUat80gm7Av9CNdHg+v/ondgsdTiFWXig9mSUGtGenPMs4aG892EC72CvbCyHW8ID07iQHWruRr5iB+l93qw2YHi3i1qIhKIfHIyh2WpHHkFIFpU6lZ8lz7xAuTK0y00bsjq7nJ+iT3hvcIQRo0PpLuF+SeUnrU/d5mGQRm2bXvg3qlumacoRn5LWow0PE8gWFOM0hLJGBE4nPgLL7KFtTKq5HSRy1FFLGtANFNBDy17Ct4lUrLi9CFIxDJufuPDef28FgZjG5Q0wEqejLYiHdTvoBbvxBB5/P6xnpTnidLiyQMRlLRbYUM+/zDnO+gMb9/2ysEdfRbJ8l1Np9Hh8uyxd7OWOqnUpTW1vbP1gybmbvApE/jiHJcvUC+EdwGxc+abBxXInQMWg5z1C5CoDSi3qRONpBSX/Cwloi4xeOtEjENpVcbEqJ+5txEsT5k41x7X+IOXgxN2S4p827xvWJWWmEdE5K1J/LMg6VdAUcoM4omw+jhsefzbbXseAoEU8goxTMMz0r3VXiEvnYQr9uAgGqDozv1Jdow+MAy6+9XKm/fqLEfHEjpLLXkpNJAuaGim5B2pEIFtQ7mVlnZcdgPbkzj1/1sicqpajorUMPUuP6i81rHY/+HSoTfYAleuZNearLXyAJ7x1c6Yhh0vD+TF4PEw+cxlnFRwi6QuxA4LPv4EbuQYM5eN7WeGGulUvQNiO6YLvYHigKpPQ7FiVH0AHbZXR7jzOPgsSnuDsCJJ9wy+dF7DPdg6g6Id6H93qXWTXyzIhGIp1NoMKXoDixBmdwa7iU+etDqVQtkwyE9e/8MIN5uBIvFcZ70SpV7QWDteqz7Wgs7aAv2om62WPKctFPo0FWizUz6kjddbGD7KRzicxjAc0G/2H1tfbwIBvtUXNmYb6Lz9Gg1fYjCrgiBlkDYKNFlysjn4jrtuMlXnbDg1Rs2b65p8AbeDSqDBGsMjd/UXm3KfjKZJaXtX9uzz7CPRC+W662mMgC6AUGPKZL/dtwTUhR1xAkzkSapUNKupE/f9IcPKWLMQ7ZHOlJxvzXvTF8Jjqt9ecB7cDuO4h4vs8glGg9QUk6MIx5SRMPKn6FKEa9zgDpq+2+iUla1pn/MQcHcfR9tYB2rQes2ahuLN3vrS8cRaYVG49D8DLBdn7b0LJCrbMYhMJtIEUrJOVVhXUjRCzjvZuWj7eh3tvfHL/ixL4BA+rDLG2w0cu98bvXnulZCEgou1iQPBx6YR+bLmS6gYzcENwjaskixqqMHzPh/QljCOhcT3riJubfd1QVunyi2705MC7Q4M4g6Lt8SxCCrkj2iuxD/jtsy3t/PWlyB+sVTmOjEUp1Z4buqx8NlhjRgNXM37oGhCFEglPbsqs6vXX87zm60oj4CSNRX2xrBoDzVagwUJwKHaqjr6/N4CS5BNK61dty/u1sabimbO7am/+yuKR+r5TSO/l4MyVll9YEhuGlcMSvSV37sJZ2pgIyQviWpcD/wFRKqQeENJIyS8MHL8V24Z8L/QEAJSjFTk1IgxKKC2IexSl3BBE4d8Cl+sHU86uNHfXy3pffN9IHflt4uEMC2yRJzQT1Shc5ofm7ir5+3UOtq+ipeHIl+p16MS7tvX4/P4hOsexDLvUzDaC6/B4g7MXv7BJn+i2Y8FfqcJpQ//pKMqDqECDb9SCwT9o/qfOm9TCdnsZwEPnNRXZiL8JCB/KJy5DgnLr+pxtPeFH0N5BoknXATlAVcgOVQMA+AifpkvmLeylv5bgmwxw8Ji7thYCHujBC/UOMpvySeGl6lyg4IY2gMkHtUmuA6P2rhoNR8d5gL32li8h5eUI3Q/7cJkoprP+B4wIc+ncIQTKDB9WOXhGV988b9+YOe5ZeZChn1yKdvm8JtZ+8t2CkLID6Fl7GWAxbG8K+8mwTq1x/tYdZGflgiUXzq5JWQ1+Dca6YBAInEHTJsO1SJ9B0dbHJZRXhHhXySggDmoDmP5cTwH5lfqTbdFyrv+PpJBpACEXZ+gCJU1VMPip83AkTmQWe5ltiCM8heMUOEZaZci/XIGXH79J4b1Y+H/qSG9+mt9mt53av5TSSVeY/GnYy/eMcQHCr6dnDts6488QIzcn2baeMZslKURz43k+8wHGgCBlDw0v8UOusS5aSpSHf7al9boGAouKiVzaLwKvMes1i4+fyKpEAHJ0bM/zqYKqKzPZE1AHoFcbwNPbtbj+UZ6t3eEMDpe/7KMI8WkzoP6v3HsLLBFcepVwY5WRcXaChpD14gn9TGsEZ0bmEflje30As8DpaPXBKSaIJRvDefuBYObm3LyE0xZIHLISqUeGb9w8q+U+B9vp2u7a/f4pVnHiDh62BgwFlbwJBjWsU0cZTTQv5z1ehuJKquZpkwf96VGm0u7+qLHr/UiRNweADpINFgvZEcLRqsfbS5P5HY8MWHkQlOplizpopgg2de4atKtfXC6q3gsFVWJv4wnokxa2urlgleUuxNE/kOjZhTBlIwE+PGQBwMz85kRDLVYZCCAi3PoO/0WEb4Wx3fH+Asune6YrMmGz4Hhk3wV1HTzE2+1j6xiDIszjmD0Uq95TVAqQWD6sBz+Sww4CJ2F8N5dhodvrPsfpmoEZdl/lDMtuXFXl2q1XdJfv1C3PltE80IVwbu9opvocY9C95dXRSMGPl96PrL0+eRpcxy0mlCBLD+fujSNbS2ME8uMId+2I494t4eProErDzvKMKRXQia4SxTp7uJcMCOlHkGEYDw7/osgX7l46ht0zQaw2TRKRknxz1Tl2dod8M4gNAWCePnuSLTt77AZfq9vyyzqY7+jDOmy9gVCazeaUAeLi7lUYW2F1SZ9Ekws59PralQhDPz/ZmFIPT2GXarZRQIIFIqFY4NWutN16IMm+eoCetMCZDxXf7HC+/AWGOzuYn85gilx/W2I3svyYF8mxOkOjADcnQIYImyI+l1umtILvaJdMf6lfRqLG1xT/Ditp6Hd8A4Zpzm1JJ7QDwS4qz9IRBJWP2qMPhw8OhMYTE3vPN3DtJ2VccndwRTQ0D8xNdgFDb+ST6vuzhGo4G08O+DOxF/mOW8f20u4MMtNDjZbmYRL1PY1Oi3tjEeRTKnm99Fz70/ldCHsbQfVGruhxpXQXWzMDVXFpKE/j4gpN65RRHonb7aOV15jvt81TWhcUZuST26YCKWpDpGZY/rXdxk7DXK0EE40fHYb8vxb8ib8khVuDQLdjii8D00nXjjwcmrPNUp89jeB3uQfsz6tyOzNJjR07ZBiNJcuZYsNDzUsyul5sYnUBohlR0xeWvkxA16uRB3bj40DPYQBWY8SdjRFbUZn4gByzWqLJabNGXLHiv/WWYibnG0r9kBw8ZbdfD+zhNB7Dk7Ytp2pb3LCI4M4ZF5/0+Eu1FGDGMm8q8qD4VDoaCeugHYlPTO7r7I+ZHd/of3Qwe+39ocG8peQBND5E0UQqJIbojYXHpxYBvyIBHpZ7pIhDKLumlUexBydHw5cZGMggaOAJXTeHPbZl9IMYWwejagHl717Il4D+OBSppoPgTDCw3ttXjX5Ixmr5ZKWwY7KDFQwEpdOlf1GIYSZP5LR4xbOoZuICvw9aNB2yd9iJuTLtZ7giQ30OZ8lcR6bN3MAEHr40CcKPngfpNNR9UineTzyawdTX8lQg/6EaPKA/YQncLUbZuN8366FkDD7CtGjYRBHZTTmawE9oI+SJRfrKeoIL6qunGO0V1a8rUZsdVEPYNmoRpQzAM84F5s8/MkxNvd1yPvjCuygxRZIUg3HMS3iEAASAcDs+q4fr5/7ecR5F2SiUAmLQmdDKfCJsAdv1Q7s0ymiXspSJxC1rlPDKdPHawr+/NCZ8lKa+zbPzNxdjY+svEFox4Edxd+XzU6RHIcm8Y9ITkD2Fb3+DDKI5H93cb+tppxdZuSTkRcEi4rPUjw2/zwi2QHxXaBQ3ScBQQOyDUAu+hctx4nFy33eF162RRP93R1PxBJ9Ba27Z7OewA51G+1UBjBeIWasPUvdDVkQbxHH9O6lUWA4n/8sXcDi99Dkr8D53B28ticmuAb8KihOCxg0hvH1TKkX7PUHhVAZ4ZY39dOIORb5X1EMRXJ4MtGkgCPQBmlEM4dl7XLK1AZJ+9m3L55y4kC0haT8CZEiITm6OxCCnua3wsIDwUPRgvR2picyepn3iZML0+VTNiolR9Q4EdCtcjRiySniFvWuqkf0ZOv9gZ3Jyat7fuGb95Fj3GZ++t7w+Bl8IcUKx9ycGlUtGut2lm29qf4go0WBptHzqG3c/y2KOOfQg/+iRgjzFmb/60uUIfNrlkUTGePN6JmiUIr4nCsB6PEnRtni7CHfVjmAQmhZTtP0qQ2q0aC8JmMNhAF7J4G3F2yPCBN+fVtluopRuWm6EZbvnxHV/m2/H5jOmAgzd6ZbvreaFLi7z4EqRGdHrZH7xyvE0Bw83a6/92cN/SNCoZhGKM0sdwzBBTift80WqlRxJqXewnvnpGI/cwZev9w5tQ1UNBnZlL7o2HjRanpWtDAWDxUjnlpgq5Cc+UoSWDKVI8alSZ3zSiL2QKljRthZM12dOcEuzxdRSNHOzPK3I2KPp8poXNQRLTRVJCsUiDCBXsrwepBihbg3i3WnC5SAeq8fQXa+zyh9QruGoNowAr0FDHHjN/ij+eAaDzk//w5D8tlmovKdUo/5AfFzgtYJr4iHvV/wmt6Q66Dv9mVgKoXqqaeklVUuZrKCAuJuArQztwCqAALBBV2WRthr0Qzfzn43goyV8VAJ5ege5bAjHRBLNsdOEf9HqKz2sfSUM4cy8nqSn2VcBJRr7KTghleg6LSlst1mYec3tOlm7kHt6S8oQzyQOzZA+wUG35mOKbHCbB8oXngLxFg+Et3X8VHvOzmCHynwJyiOIUWO62lyYmT1WS6m9FNZYrL9if1Mf4rC3P1fbWF5KzkIVyA6Krr+NpLDnLHSAbxMjNsRLUOcOVk6qV66pmxZe5fSXHRJnbiP/RGWqL5qcBUgIOJE7uN62rcSL+M6Tk7j8Wuq+1NtjOHbbZoUkr831oWbipTMfTw8ztkcEPthuh+sfilGtGSgza0yQIYI+C1Xj6IK8OOhhSnPz6/1A3X8BGj5jobPTE/ZW7yp+a7g+/tMQZrNwnpeaq+G8hylc+Y0VVkJbSafIzjk9oORTkg6wYclBN9oGodfge5+C5EUhQpPPUeIcQj9OV+kCPi68grbETRGdU908HVkUlBF3wGWQb0UAQBe/C0n7NmF9zqVugsTnmmRH+0NLXS4tNV9AynFWP5/9KCfSDLSFS/YhU+wXDZyKKdOEVB+QH4QyxuCMP3JnApMpROv3M2XOV71D2vC9b92MH6VUYbSoMoswOZ+w7A1YJs2Zgx/pUzzq9kkfGFAxQWHEwQRYp7HfF5nktHL7GGwqbbzWK2/IyfeTM2bDDV5kqukbVTOCE82D+dA/kALGpgfdKURFBhAmd9dLaxjyDZLyTvZZ4AmEn9ZhKVxwOPLsIekm7BCdwyn7QNFsZVlyEiYTSfaFh5QjbEBXhniHVefs0gmn8smPk0zmsRx8VWJEn+o3ktPdLxqWUSfE3ZTBVgBosCmbBR1lqIXxSwEcGzHGLig+raEVzmIJ8bdK4VHAkYt9P5JyW/ubK829wQAdyVFJXj+///YOrXxdWFBoHiA8IJ3cS8aVf+PnbpC76TtjoxXAFds5eJP7nPZLDrs9lJ/O6TOlb+LplOfyDoPXXQZo0Nxscs2hDvxSKtvKleRHPA8DPxC72d6JmdQLI26kbf6C7bCaO7P5uWreJr6QwMfk86/ImT7kjAYIeE3mbPHz2dVcKfgZKpBbzPVWVoAtz7bg0IQuyXWiDgELPl5mOkkLKtM5HwhvKbgr/lu1ScGCTZe658PbSRyNcE7aDKhfaA1PcrcPdekbs0mfVVAyOLXn9Xaes0OnoJyFX6rCj5Nxqg9mJtRbkbbrfFbHym5xnHFNg8ekda4a21IJF7zEc1yjwAgXcmC/7wixHpe83DRepRzOOanlt+lf/UKPcBoiww4j1JittaxJdLoskbEj+mJfoUOxl66zvabLvngSbhyfcURJgn37rs0zhsr0miBubD9ko8Pq6EL5mKc8a35674nZLEyODL5K4X3lkcyPFCrO9q4toUo5dtmMVZfqAOAl2O7kfVCfG2z4I4eNrO9SBCGC2O/wCTfbO4CMaB58fx2SuRlWdS0+QOqgHBy97Nn8FKgRkNuttOZs7I+UBrKoB5cXfwDLUdDqMhvvt9vZZkbgGbZHEcdz/f60aZZErMFjfNDr5/Jw99MneDqKj0KN1EWnTThvTCx36RSxnyOPePP11qLABoxgJ5kbDNyHf+uS5cidwg2KkPNV+clSqTzdDTEb2ICjZz/Lb3saQ/6ucfoWjh+OdYV3dlXxZFZZnXHNJoMgAaL//wpINX76A+7QLipp5T5BLh36uh/SfIAmA3gR7pQVvKi7anZ5KE17DAzX95MxVlxBeupWr732iMhqm41p50ZNkRotrAjV+EScrrGDSm7vb/bXmtAmx/Pi5nhHiKmRdpl1K3vxju6rtWK11LBY1oUo46X3ToTC+iTzkRPqX18Tk3emC0opgpgpOrzWmLtX4sXCi6F9O9MjYv3nbPQ/XK7sMAuJQzwCgGudeJVVlk3DtAr8l02BT9Nx/IHKpqxrwad711+aLHfKm4/kl/GBywSkIkuW6Dnc0HdNcw5CuxHjHe7c/ihwkykDleM7wZWt2WPrgjdFtIx8d98B7Hoou9r3LNq0xKqudoYbjt67jy3aXeI/E6Sufb7mO1iYLIrz3xtEYJXELxein8iAtQq7qqhb9IlSQXapJ0SAt0xu0wJHBhSRLRFs1//2Wzo/ICcFHFIL/E/E6t6dWp23FuotN1BAdNdaE1pL+AaQ4ccShmaGedUYDm0t2MPQTZlE4MZvxBQSCecOV+czMw/3uFoqfW0nKkLK0MXLFFSTrqpRHgmupYIfh5aMo17Qlgy0zk3AcZ9gjNnPBAm9YqSDx+PS5L8oH8uY8q6WJNUw4vwwAVzzrDxTibmlYtXFg6qH5x11dEvJBIH55258NcVzrn+fcb2ECqkIQiy5Xb/62SShKPYosCle7YBuUhXanPE51KTNZaoh4+2oLIoS86KVpdRAeUp1iUmgmpiB3+vZAF71qKnmlaUkgGlhzmGJmpDyN3hhxOtWYeKMRxu977XwlT+mEvKJVfbODUm3pkJlPqd4zOYdRghjFCh+FxGtaK1+jMgIvxCQLTlbkrhoCuvVPh9gtInouBYG/oAc1WgrE+ZdV3PV0JkdOc/sVf7w1GTLVeqA5euWs4+v40nK5JMwwruZfNOMc4JWc1jBT7GeOeOgdRAu6R/KEhst7ByLu908YiZbcolT3XNDPraeKyeTWGZQ1DxWGXdPKWmmvl5NwlMbT8KcZIoJRjtyrpg15yUsXk1sGnTTItAm4JMleZF9oZ/vBPYbKTeG3Rt6zm61EEtoNZje+tTzMFXlxhn64CAYb7VYBfiu7MRewgJBkItjwpaG1WJqvwF7Bjiqd1oqBkQdR0427klgSyCz3v9Lw7iHXbJ9sFpYmjZokTpzc2P941rOLipUlJjbUnrtCugLoAqtiCXWYC/do805A2x3lG16RQrZS16ISk0aZH6II7udvyKi6RzkvbNVzGDj5jK0s4ZEuG61GPKMd8Q2wuQbOpKSjPOTcXu8+KmJBeGtC5JZSF6DmTPhSHf/WR7sX4qfuERGV5nV664ugldjwou7gyQPzE7g5NmqpADGWuucQkQZSRwC3Ew1qY0FJ7jcmh6EmSgTAsy9vSTvv6cyPFGePYnTJe+kqVX2xykDK0Nrs0Rcd05uKiaqYkDKl8NfVP4TGXnens4qrnahEwtCu9OizW1UEg+47gkSLa6nZNswak0F3qi4NpE+ToFVca0KF/Agly/XLJw+YmGxYF7KPLJp+5WoJRe5/7Qor+1DkQrBJWYecyWYEWia+8Ypr3kHgwdV0gYwcuBlN/OPBPlwJTUHre8mQjgFC1msgQlTjodPowddITlt3zQnZYWFa661ptuovfbChzaXBWGlNd1wRHWVWi+518Nbn/rjkj0ful0XVV35diaJc/TYOtt/tkPMs81r7R4KtC8lm4RuSj5HcjXtgfkJCpoWD3FVytN1ezYQO/99udAaXi9GA3COiXbhrSL6IlWwMtlG4IUjjCUQpcfJpEJg7u1bg19pUiNoXMrLyxaDiL/h5FN8jW+0vGsiJXRXjyD0bPBQ+DYODltWccVWmI2+Bz7orRRAvN2H7Xv8xtU/DHzBZKkJODTmjuGZnVfz1RAGe423LHa6LzPbVd7cUepkwefza2hJB7H0ovTQiPooUOrYTnN3drhytLsJtmkNzvNi6uxaOF7FYWJq7KgWoPUD4BLqdwndye1RwiMHk+soqNZ1jKgLlB5EQ+yrcGMSX0jqXr1YNMITiSEN0YNHG3vk2C01mo1zdhk0bhjBno9q6YmIXw8ynG1UhzX+oPqwQtAHFtRr4kzTF22Jt9n3FPGb2Z3NrWKMXgQb4QN7XIiM3JSxndIpHSHW5ldSSDUqc5um+XdpvyuLUs5oqyWuztpNtXq6D9IHMyJ6VKAcPJCYu8b6SFrYe2eryBbm3bMODBgF3ILPwPtzSyA3TN2fcDBgkU6/I8esS2iC707rZAyoP+a3iw0zfqLvtrgIxo6sHPeAmg0OEQocJB7sPr4M6mRxJEvna4E1BG8kFZed5DiPDLPW8B+/9Jo4nJrKx7RQx7akLJGR4GAIliPLY4GXr1xsEhlJP7/5MU9f7TXhgon6IbU05i6vj/QgYKxybsn2jZ9Yc91N00twrjg+gCa6mOI4TFoYx0oWeXInk8kzSOgxv6I+Sc+vLuB7D3LVtDouqPKeSNi7w01EakQE5YKSBDLgKpD0ZKECf/8WUGiQzn4q50p07YrWAYol+XNO/lnqrvOl//obQ/zZ2jIYTt6PtwDFhpJU6u7ISWc2rNQ4et8x2GV/lENE4nDiBVgeQ3aN8P87gQ4nbhZJ0XpwB5Hj5vPWlRlnHIMy0Tqxe9a9vEwYUm0JH936H4QimNMZCx9lMw0VVFIHKtTqoyryH8waTRFlhgbtSzL97HwUP+nXtoZjh8A5/84pEeRCp5suCaC1i7+4b3TWmBVunfa7K5uUCgeiEY7s/GZRujKU9aRfRwSm8VrxFD6+Q0lOKhgVffNVBKcCUspjX2pjNmkSgUjvKRo8zbS/6uc0o8yb5vNwkyNzs0gCWsSl3OtrHQTiBT4oTzi3PREu/KAUDwpQrWr3I8c3Ljrw25s/tZR7SBsco0LDyKgnmattesxsVk3jzA3gnIqqV/Rbba1xuFCkfVjOmVtg8U1G2JY1y8ss066tIc9sh7pJ7umse8v8NdL9jxVOIhTYL037o8YR4bNWEJaH4wFCNqnMh3t9qOnCvvVUgRrNLQgk6ZzUegmP3dwIhBP9VVpwpxa7Y63yGu+p+FxGZho0ufuhIrBRnJOv3wBDCthKFEVElhtx6sYBndXmMm6RppinLKAj4FhMykeeL7GMrTmj54DwyuXt0DAfFSNdtRZy+JQwtDFAH7jdf5Ht+h1u63NNYQ/fWgZdckdOsvAprTqLnm3tk2DXmavpckyWr07ZJ2PSKX+h9BbFvlENd4P1ircUSIy2DGg7dyvrXOcGDBm3aJV6SRozyWqp7ctCXlW3xVbyhNyLnyFNOnxhrMee5O1BYtpd3sDBaBvGIVo92ADrKhZ5rNhB0vWqJFqpCNT5Dd3R/hu6MJHa9fx1LKsKcerqyQv1jh7j1LyYEqt7EzTKjJ6V4bz6lBExegug8zXUobjQ+XTLB56Szbm0UMym3FJZOhquifUTGL9F+jejfRV5f8cjCBG+CDjIId2Skt/iIX1ANBDqfczIpV+SPwGBgJx1magtIwGjF4nMMGpAjobH8s9+jXC4F6MKtW0txHrnK/9y5xyB48ZNyoQ6KjzXu1iVWrW6guOcGfafk66RFb3phDbwAs09H4rQxjSdtQpXzlyCrCAcnT46Ndp/4NeUep/KZFcaHPdxZuQlJUMgwtQBDUbjzoBjn8huNBkEVWvXO5tPPjh/gWgJi+LkTRn0l+XC9OsAeb9QNMqg6c+8I3hXvVskwAhvfY35yJxUr9vGQHHoHCc+UP8lK+maAePvcsbNmnxjrRScLmHA/RS5AwehDa0VSb6dCNdw2z2xuefpVWzGgiWR4IK3i3FF7A7z/YB7vl0spUHxeFJT0+0v3UEoRyxHJyCee04dQ7yT7fHp+aDaZHBCFwCm0TcWUxBNnefjg9y6tyzep0ispQwyU/qwVwqWWTJmfy7RJqe0advZsU6IqXwGIitMQBCCjL3Ny8IphDck98pM/ooLIjjM4yGu/qrkSbzAQfXf3GQZP16rH9DZ2kbat6xfw/wnrr+bhZb27xNyNZUojKH3ZMOAwvuj2SAry8NymAyuimQJ9Xm14xq0/f/ki0rfK2gyD6AOfVLN5i/DvBCGxbVH1AcYoKY5BwHVkcFO3iAL4kdW+sxhREyTmRMngrT+7FWGUCKVTqyrLCsBPs0ntMziaSMKcSyXBf7Y7Zn6+28vkUPTFp8Y9dg9/NKx2hNyYTetgqR79jp+5v288L42G/wvCMZjgXtowNFeTZai6qE7irhrfObOYvufWPl0dEJvuTXBCFZbv8Qh3IYyzO854m8kynXFTTsRAKrOzs1T/ISbcUztdT6wytBPj5V2t+MXJ0eQmwJRGvxIKScXoD2d8YCYZ1SyLhOusE1Cb7LRv9bHz74VXwL9RnaK9SOMHk+mstmK/T4mu6o2Xmxv3sr9YI5s3L2BCQORGKcVJ5hqSeroC7GaHEl+qP8UCR5cTK+Z+bT5EvODgfKYg4P8dGfOJKsViDsO2YyHFiu7zvKwuVnX1cr5g7jz4reW7Ze81JlZkWxCuJZbNCowSIt/X0UMWs8kcfaSPNRS3EjXu+ggE5T9d8I8spFVj2oKjKceaxRO4wD0j9jjzNGWAMj36QnaV8goIvJtqTok/I1q0TUqanzx+7Zk/3RLCtqFFitQHWBiMwsstyXW2tKVXDVwA4oFDcm6z2FJUu8j1i6wkzqqveoT6gmUrsgZkR3q93rH8DMPB20ZQHXcp2kTEsf63aue+zXhYG+692g5awxS2vHQBZ2rMApIp8mkyA3ZkOR4ljzqd0szH/bw/wAOXWG7blHFuCvMretaIdPx0/4ZkScN2V5CUvd1a6BaiR5EK4sf8TDQmGUygHOIjssHnmRqNoCGMCiT3EjNJ/JPeO1EX1/Cu6eQM8mK2jPZkqV4K8D+IR/2EGUgI3HhfqacVG4pw7t8rtGCGomPEnfy/4B8caKxZf4th5bS4flWvhj/Y87+U415PDGiG5cBwfanaj7C6b1ahvonJpNW1QDnYv/SNsg9Mj3usHPBm9vgiKSxOlEjNsluPaMNcU1e0mLvdGRUL9p0eylOzxuqld4ALmW+vDCjNUOIcN6S+7Z1GIcXsrBk1cl7JLqBMMBlxDJuDRAHXeVuz3EZbMQEZyTBaxhpIEylLxhA3f7CNeiRXc4YIiQl3Bvz0v1pRQ9cAeFD0kEC54V1klr7dbqXxEA9G+tbHDBXvHaRAdQVRyjwLvkxpnZyvXh0D+mGEERsnsuEAxJscNW4uD7xvZE5fgIwbP0jY5maCkzANavPWK2GC67me+Z91TYBqpr3tJ8z5eZ+sPMybI+X/wbFIXW2hrq1uEBn+y1z8jdkjHRAGZ0b0uyjtH4FrKhYCiXzLn+5AGZABntR+H0mTj6EpXPmYUdak1Uj4Y1CqcamZQ4uR2r3atBL41xS0rGJWjX09Q2zKJtdd0ypcj4ALFLkXJHc1RydRogro8cuv1t/0obMT2x5ek8EJbbqff2whrHQL8WL2vqZeF74JijS7YrMg180O4RD9JddEo/VZijwdKTei5mIBC9YnNwyOl4KHt5I1rbaL79gc0gUWRArJABr47CHO4sYh61VTRryD4IxFVsYKl8hDONmPev3Lg1mTJmh34HDmAnOOcjFf4XuLi4CqwzFGrwfIOpg3eMWNhv5RMyKjAO8USd/7SGhzUbQ/lVauZoKoTeb/tMGH8bJ4QLe9lS1Q79JIyKzI9hMSavzb39GAKMbnHNbN9Vr6pPb5rjnUTAacfWSJ1LRfD5YbsDNOlatKfaLLZpWueB7O9GXBx+7NLHPdmq2uoWkKJhQBH/hk+HDvLqzGUtzYaZeXlegtzZ3icBH1FrSKho5Hv0XbEKlSqodVBDMhjfgQeF06Q4QY4VL5Lr7kLiJxauT5kL9rd8jOWjiAVaA/jAA3ZYTLYjKbl6uJvQKoj1wk9AlvUVdDRibpZE5vTFESZfzFbvytKcNX/MS6Owp5LRaBrvqNm3S+8tPqkyBTNyCuxhppdcJmw+HXiDFWcJeRTonSvMw31Yq0rTZIFqxNIjVCH2TBQj85OUPTPV7NlymcndgS1qwTC7eXWwFD8WrBpnA4UpX+itT/9ZSjavehXGp3j+rbSMkuQ6Ev1YCneVzCHXtBLkhEF7ye4Jg7Wp0z7Zn1ZLgyxPL3Zx0jn10g3rrKL3BaHkong7+Z4CDAdPq2sCtY61SLXS1Vjr9vjrVja4Q1txknXqm+CWyducw9+LNIsRKXi1h8AitSyZTl0YS4CwgAX0Yet905gU9sIVoY11pc9KU3eJeJtIm1cchWtfUDlTo+9OGSJn8eKl9I7zB5PPh0x0i4XDcB+oz5vZTprAiN3iAI1i0eQxxYf39V9uIlsW/y3grcnOK4bZJG/YSLSFQquR+30kNUx1scM//TdvqzRfAAOSPIJjFx+y0wh8YAtHHEpG8bSsOz78c1UzfKsP5AAIAjkPby9o87JT4h0NpY7vv7no30t80V9VKSRQD2KBgNEWK0JRcXF5lN1IFOjI+jUEm/buWriTKgsVRdze/t3t9oEOfx2/WsRtIWZJBwQUbOrGGGyY+UQWcRGx8qjce21zp9ed/i01FZ2WZhC1RcsKQT8p1irVORdMaaloWogV0DomV02R6Ao88YHgKqOekSiKLt5TR+IMcctlvFS8TEbcIjEOO/1Dzhh2jjYzCaJ8pCS8z8smebHGXuSujpIjGOY1fyT0oWacNm9brD1MVg4eInU/I1m4aDLX7vrrccgH7rqwabQl/GgtFZZsLlBcgSdIOjN2BBWSNGgqtc9p4hLJd3GI/IfdgmnC3qWTvVX+gtsSN1fqn59yXjJD4Ii+1q+TdWWfx99pzwqaw+db/rmUV8zxu5OtxJ7VWZzgrZjEWzYaLtxxB2bWbB7/FlWlAxqpsDMhWjzEkwxqR3E6huoOB/HQFhmivKU+oB2yOIM0DbLaU90h1tkurQje0aw/EkopYpCSMxg9VazrusuccHseGW/gp4oQ3wDM5Y/zQxfRc7xetbrSzF65dzJgcFfR4s2aq9CvmZVlkNWQpoe99FN+HTGLK7+t49V8NvGyMTd8KszbG4eXlaHznpDj3Ls5SC79akFzucs/ZmRSHGVaYLsx9eZ1BwnkauA9pc9X5TpwulU+IIa8gAImImpGP9K/1SjKSfOqbQBzGcS5c3YMGa63g6N2PpQx8/ylP4pyh6uG9lpw2MVhqudGyn4S8UsRsunHjoaqoQol19MgBtLWW9rvU+Hy1chrXkeYCOrXpte0pL/ODgYD9CQ7tqjxzFicZFZekoYRultTnQ3shxLxSeKxFKddORKMj8xmlRtLg68PUobGzzlM6kExl79VgNCEZggAdzhfbdZuuFoBiLvl+nITFqV30RgAXa9tcvwY952pv/25Fh8GSXgmBNKv+U6zhjmPMJirzhdQDGRrqXW5WjUsoiMrugiBrsfN5gYazRcyqdo+2U4G/YIXl5gWCdn8pqQn8db9aoyMw29gQ0xUIeFNj25XHZ5Hs8vvvEcojvAaOZLPxT1ixGlOfbWv7MiH/YlBdo7c666LFuvA3YGBMV5rp3+Cz5ZDv09ynIpGfn8foXuN0k5id+dnu2dPLYq+7JdxFmIaLTiIOXJQcaBVR2RXHnqnkvq0Sl4+N3imc+GGWZkG7urtYtDCgtVJJ1kyGoecvVRzNADV8JKygSKDRY+M9+edk6uq8zgy6p7cVqvdLAm3FJRaVK1TJyLpWOJCwjKTLRcdT2L/HgNC8iFkgzWR4BRehBeRWVhqm8zDm56q2lSuOjfKd6m+jZViG2cLMZL0b2Ik0g1I/JlJvnIeob74tE2+ZwKamKPYyTKRW2aTHuRmJCdK1ESCUAKkMbRLa+uGjM4TfIRDJwC3c/cSiLnojTWj5LWw==\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing);"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"db2RfIwt/EgN7Qw7W7gyu50pUYtgaOPhhUk8C6UJ85BEFwjrBoRLCVYHwFKlrlvbd5LLU3crSpUtkMwezu8tC7OELMqMgl4f77cvqql9yCqIwP6kC/ULuZhqeeBcgqwGa+QRh1QuTT5atClVUk3/pBNRXF+tCIKqQtKRNXb/R2HRcHn+MkmmITmLw/csoSsA0bBdsbUuf6F9FtUM8cbVWOQMbo6lL6nevnqWOUmKXqQeuwWCaNH+mbmgu2gGruIZAiO7lS1NmcMx1HblJj5rwhvxfbAnWqUL8bc3tPYQBT2Jwtaz6GTkGzXioY1yK19rjyXZQcXI3+gomGjZvCSemmSXjekS4II3Cp1ZFw+Ub2QTfk5ih94R5nWUxIdC5lbXyVQGkEVlhh4mfulIkwuSvKAAhzfuFeP0z9GM8ghItDrRj0QCERiukneezpU6gh3R25N4pD6aySB3LKRdMMWTIermlT96vRnAGl7zREFzeoAvxZ/Cukt1zj1hClPbz0ZIBuz0vHzF+Uaonbr6PpO9ZNSvUmDl8VBiqDC010js6ywQuRUABc8KmUVGuKXiPoirKTNlk8gdl5mis5hkPfAIBlKYgLcdMa1Mpypi9VAeVgsGxGCKCmdrxL1FwTVwz+mefenlsta1hSpybw5u8yRkdUFyOI2Aeahim5oXNDeIJjxL3xV7CGxvlq/2/ZNte7KZAfSWg8CtjJumFx+G3EaQlBLL9Py+ereiwEy+9cWZX5AhsaPrICf+gCxaODregS8wPK7CUpvtjG6hcCN57fmrVSzLEkDJm6jCADghImUrf8U6khZeILQub4NGUjUa7/KWkNUkAVIZ0ibZUMs4TVMD5un9AFNcqqnP8zoP1Li8RebzbRtTgXoa/BQogHCDAf7jFBzz/YFxphIEtWhxnwlJEfntUkvU/JYqwS+5mEg8TpRtsWZ+IaHsLtaLSwwZ71K+rAwOKGSoDcbPvUzR119j3vaVhljpM0GTLfPt07kJqHEN7Sp2Q+8PgG8MecrdTVAYYNFKYQAYuU10WZ9KhzzOKGu9Ziyd7pexl3ZDsDFnm9jS6YYB/m2tT+JybP3MTPNv2GTNwcf0A0hpc0iduC9z2JNeytRqxmLQlX2myRHaEYt8Z+Uen8GJPhZ6dosxCa2f64dv7SEYJrW2+d+AfYX2mckOP3PbQnLqJaEnKUMtkgDLLrkz3keWc2MsgjgvEE47zpWliDu1nshlBOlH6fVd0pMbyd6Ncv/h7NBAzPd0uJ6HIWBhX5vyxSBvGP4nM86xoYyfsADF68NEYhGBCE5pCDmc3rH4QrVVeWYkUHXNGIdYDsxkvt4i8jt/D3Y2cK5KMmTRtnmdU2zp0N5LUTKOHPvuFMPbaEGA8lSEEol+F24RkFFVywB8k1k40pCrmKGHN748U4iJEHXKLDBgFBRngMd2HBfK9W9Q9sPAAV8kd3o0h5pHqdevHFdA9a+UDzeALwwtz08NRx5Rfv9ZP6Nym6xJdQ6qmz64meaXSKtyydUBJYrWrCVmd9KV5MdACbLL7Y2zGGYUvgunLQkU5Ixgd2JupAt/TSfXrp2fkCaeIwlT+4Yx8LSDuNx/q6/CiUbniZgWbZOkRVhVFYS929EI5kYif1/cz4YKwN9kLTsDJQFryJQl6OEuqEfNscxeQtR93hVIqEThhuORd/tsJwXoCOh21rZyB+fi1MVqJ0G/MOSpuB0smIOk8DY5o9gurCO8KGDPNjp+lT+zpz12j20dzCtrCsdohBDL48dFgAzPxkYxI3b9RejL/ilrQ7pdPYOLstUrIlYK2q5bxOadd2SsT1935UKDUlIWr5zV63T88N5qEdsJ0EdtM/Ke/1SwvKduyasy+fbNNa17cMtRIF4t58XgriqKT1Te1ljUOb1Lq/yHGL0zYn418Kyrob5sAgTMrFGgr9NaNj7ESlhhQqJntiwBoGCgGZI432Q7Za4vbzyoFodKTK6CgC2gZWofK2zFb1QWSc5vXdQx5Xk0xvt5eQun5mWOQ7wEgljHYIVfo+NSQhnS2NiR/U3ae9Cwmi/NdG3gJpvK3fLydaKAzED/MF+nvG4xcCDxXRE7suzSObhiKrfYJhbahW8WWkXLHf9/+m0Be+nVA+kgOzq63UBVleN1c5Et02zecEnc8jA6vAsshyD6+O3WVbOxxqAo4l8P5udkH1EIFfPr1iXgEWHif686shj1MiHBcqiq5+vhn08A6VHbZ3P2B1O9T46jwvWvSQabZ7YvQ030gMmahCcgdkWLkYtn1ZPBwqpxs0J02lBG1An1kj+kUyOxu3+41yFc4UhTJ+EuYqY973iROZDN56R7hWefrxX0G/0WpkHpQUtstq0+YPNee3stS+8y9pgHqsZNdfCCJYib1IPrq0i5LsK114bWmLBQEZ1tVPHE8vZzratOTfyJg9TX3CJi7Afk2YHoHMjCzaCyOYnH3ym2vaRdzOwC2S2I06upCM+HQAqmaRMxqF/BLTeUsL+HkT52Bxq3UnZGQ1SWUDdya6IA0u2p5TOu5BFs4/A4sc7AG/Q1BWEIXbmskVWlgk6XJpHU9T+jO3PxPsTWvQGRtS8O2X7dBMCbyY99bUog+C5ljzSbPIBuIBqZKZJy/98dw4Lq5ZCBisGQi7ho4YX9svuKaCOs+Rmh2wwYYq6Fd+xH63B8DY3VsJr7Vw0qn+qUyvIzUU/S80dplg02jdY4rNM3TqFUzy1NPK2jYIy8ZIiddmZR/TlQW7KYszpa2PMPVxFrUbW3wiqCh1ffhM6JfRr22yW0L05uj871nZX2zUEkccgDC4KXN9AQ0V/WKP1of1ulo92ldHbUk1jhkN3to852uH9JkpTHniMeR/UVnaZjpalvIcexXENYYhuUVV4/gpMMAH+CYQvINqnTMcONVNR6Hrlob+ojMBK6poeGo/r5q6MnDnOaVfeFPKspRT2wA5h//oDIr3kWg+kfv9/nv+9ExN0AF5ohBI4xRrUEoQ1r/GoSn5qLYXCb+Mta3YKsmWPsMDaQrKRGz2YPA+XmByUiZW3wgpz+o7lJa4aWqenTpabgHFpzlMwlZi9/sCGPiAEKelI87jBDgMB6bSFOAhatWKHdRQecd2MNwS41st8l7WT9GhOK5LNBvqxLBeUd+5PYFIS7wYXa/j//5PFdt8/OjBx/30b4aI5u1GC1y6RKwac4HUXLuiAPW3bsD10/PHKFQ1Vo+wMNyA5yy1P6geHZSxphHGlGgXOQKhXou8rczI5PFetekzJBrVPRIZwM0kEMDz0xsbNDwmAIlwMXvyszkukS7qdJ8dRX36hWonMGbJ4xJ9sqCzeQoznytO/TkmUa0MRtCM+Zul7X+Y0i6In3qsntz39648SSjowzS+0gaXzV1ZJVwoboSefVVNp/rODgByzJTTv4+MtV6rJUrvolFH9ORQPhckIDUNFE9wiWMSENYEs67vTmFBcXIwdVRaPf2A68IhG4IPNqZb5FbkGlKdzkltOSJ/dSZOYhOeEA68dCNLmsAvu7xzOAR8reMJM5zUStVWGBuMzDkjYBm4hnANXZ6BXfHDXNMC+YZ+dFvRZXcRwra1IEcIP2DanKa/NVGn7R8iy3Qv7qHJhPZtdU+rq95KEGkZBLWfrZKwVlMtZ37yM3XF40OxGi8moNJ30dovwNdDLSVtI0ekW5i1yGXZD9bXutYm8TZJBoXo57zdVcZiRQFaMYVF2jV+HWJEzx+FB/tUwhZp0jBSiAk2pr7HGbNmV3sxPh+VIVP+Nxj3ztqbHXY0/wVcWChp3OYWuGfKhaqymkFTk3ULWCyugix1BiGRxp4+V0HVAgI14X45u4F3HSGeSzAkf+F/vuQn1NDDU4F6/qc3fgpsEUYjDXvftZIttL6XSmugIgWXmo6RSIuhXs5pOisBDJ3fc1jNs87o/6TVrDY3wlrMiIjyIt7fec3b17NtutdIJVSsjd0/mfrbMzzvnMyKK6Q34lxoLWwKIejYikfPzwSX96jDfx5Uz8WTq9nBV4TUQpb6V6Dd1xnmDnDT5qwjIpZLHwivnaVg7v/2EisLe4lx0622aTAVRFjvcI8AqqWPiSVImj80UIkcOEdJGkVvjcoC4Qb/lzMZ84SCBBeCilYy8KpXJsK9Cu1tsb3dGBR2GZ93T0Alomumjth8xMZJF8CRBsQVPEuZVwzQjw3dvleELz2cT2PtQGLA1+t+QiElEBRYrW7Bp+jOuNu0blzJbiFpj3ODT7UrXIal92iWflN0N5AakzouOuQaYoFEtJTP3Lc2HDLfWJkPI4NrVMFxD3OV08Mbo3jPNMumy3VmjcNOGtfWElY7/KrlHN6yfB43NBaYMqbArIOdnmkANMgkCxoV2xbCwz2xC9ksKx+EtKXHmBp4gs2vxlAzBIrsOV5wjVEIIwavrTvlBmnr7vENLak7usnBLPSIXYrkLOYlpY+7J1Qr0Uc2N6E+mtEtqyb/nDAyyWluOHvOPN4ZfsPByhye8RD3MjoZhPsfRYY8mvZFpDR+cBXwkA3MDDTe1ouSfG4xYvg1b0RTYZ2UEIZtBWXSWGysSRQLGbBOU6fb9nl5WkVKjPCfbrckOd7nW05FHKCCeS3cUh8ILs4osr594ti/yb2gOm5wFVeFltpvu/Ojp7Fa6jAPSDIehV2aRdSI92YQEaagosCrVRDg9GCznZ4c7W1n93oASyIHOyD8n3XF2oAXk84FOVbQvklpKPt2ASRmlY/p8Gb41rxC/ZEDLxpx3cZHOUzr1Es7c2jG2OKTFFauAebhflGbZOSBryUDamjS5Twu/NvF077a0vJ5pyZBorBWYS5Rb1AjXuCE0JCa4TQHy542tlll35tB1eW9FC6Px4Qernh0Kkq+hJ3m3uWe867VaBcFfsWAaUFjtF2QEpFuZIswPveQIeYCcVL4CCZZqctMoeOXAgdM5KLuGV9Xp0r/aVt/T5bFwBMIjPVFtSr1kT7nvip44jV0/SmqmgWtVupfPqyNJqA7EVqpjegNWcJ/R2zCg7/npXSsc4cGQU47cVz9sq8qQAQmZwRSg4Ow0vLfQNzQwNpjEYu1F0ZwQFloW9O56dvdg5EWHsJL0FrvD2C7rgeX609NWSNSD94VJ5jxZrIw4IFmjARpp5oaCrxv0bHCygTtm1YemZij6KqyAKW6o1he1TGACJB0OMFVRFoDdxaYFKDIYCazQJY66nf/MhrBuO4HsgceaSqlO5Q/P1XbcUbzx4wc+RoE+S6qw3DgzBiUBzoMwgxjuT/k3lyOTShqvdwi9AgXa/IS5sfOC9LXQf3N5bvhNOE+O4Qd4M25YKz2gXm9h4jnlMwZkVFujrnQO4f36AMyY3aXe7/ulaY54RD65G7TiXwsMH0YQ2wj+gaGgEzzLjXCkyQt90ZuCqC8E9c+XqXsXikAHYWDM4Ga4YkcXsKkPfre2IEHMpoI3thgHI3PBIxxCQ/UmoMJids/K9+jdfCfzdNatyv/5xpdHqX49b506xEmFMSROas89a8/96r1KZv7sbvSZmmnrfDcmbpwmzZV89O2eLXAtdYgqp0jHlaYVUat80gm7Av9CNdHg+v/ondgsdTiFWXig9mSUGtGenPMs4aG892EC72CvbCyHW8ID07iQHWruRr5iB+l93qw2YHi3i1qIhKIfHIyh2WpHHkFIFpU6lZ8lz7xAuTK0y00bsjq7nJ+iT3hvcIQRo0PpLuF+SeUnrU/d5mGQRm2bXvg3qlumacoRn5LWow0PE8gWFOM0hLJGBE4nPgLL7KFtTKq5HSRy1FFLGtANFNBDy17Ct4lUrLi9CFIxDJufuPDef28FgZjG5Q0wEqejLYiHdTvoBbvxBB5/P6xnpTnidLiyQMRlLRbYUM+/zDnO+gMb9/2ysEdfRbJ8l1Np9Hh8uyxd7OWOqnUpTW1vbP1gybmbvApE/jiHJcvUC+EdwGxc+abBxXInQMWg5z1C5CoDSi3qRONpBSX/Cwloi4xeOtEjENpVcbEqJ+5txEsT5k41x7X+IOXgxN2S4p827xvWJWWmEdE5K1J/LMg6VdAUcoM4omw+jhsefzbbXseAoEU8goxTMMz0r3VXiEvnYQr9uAgGqDozv1Jdow+MAy6+9XKm/fqLEfHEjpLLXkpNJAuaGim5B2pEIFtQ7mVlnZcdgPbkzj1/1sicqpajorUMPUuP6i81rHY/+HSoTfYAleuZNearLXyAJ7x1c6Yhh0vD+TF4PEw+cxlnFRwi6QuxA4LPv4EbuQYM5eN7WeGGulUvQNiO6YLvYHigKpPQ7FiVH0AHbZXR7jzOPgsSnuDsCJJ9wy+dF7DPdg6g6Id6H93qXWTXyzIhGIp1NoMKXoDixBmdwa7iU+etDqVQtkwyE9e/8MIN5uBIvFcZ70SpV7QWDteqz7Wgs7aAv2om62WPKctFPo0FWizUz6kjddbGD7KRzicxjAc0G/2H1tfbwIBvtUXNmYb6Lz9Gg1fYjCrgiBlkDYKNFlysjn4jrtuMlXnbDg1Rs2b65p8AbeDSqDBGsMjd/UXm3KfjKZJaXtX9uzz7CPRC+W662mMgC6AUGPKZL/dtwTUhR1xAkzkSapUNKupE/f9IcPKWLMQ7ZHOlJxvzXvTF8Jjqt9ecB7cDuO4h4vs8glGg9QUk6MIx5SRMPKn6FKEa9zgDpq+2+iUla1pn/MQcHcfR9tYB2rQes2ahuLN3vrS8cRaYVG49D8DLBdn7b0LJCrbMYhMJtIEUrJOVVhXUjRCzjvZuWj7eh3tvfHL/ixL4BA+rDLG2w0cu98bvXnulZCEgou1iQPBx6YR+bLmS6gYzcENwjaskixqqMHzPh/QljCOhcT3riJubfd1QVunyi2705MC7Q4M4g6Lt8SxCCrkj2iuxD/jtsy3t/PWlyB+sVTmOjEUp1Z4buqx8NlhjRgNXM37oGhCFEglPbsqs6vXX87zm60oj4CSNRX2xrBoDzVagwUJwKHaqjr6/N4CS5BNK61dty/u1sabimbO7am/+yuKR+r5TSO/l4MyVll9YEhuGlcMSvSV37sJZ2pgIyQviWpcD/wFRKqQeENJIyS8MHL8V24Z8L/QEAJSjFTk1IgxKKC2IexSl3BBE4d8Cl+sHU86uNHfXy3pffN9IHflt4uEMC2yRJzQT1Shc5ofm7ir5+3UOtq+ipeHIl+p16MS7tvX4/P4hOsexDLvUzDaC6/B4g7MXv7BJn+i2Y8FfqcJpQ//pKMqDqECDb9SCwT9o/qfOm9TCdnsZwEPnNRXZiL8JCB/KJy5DgnLr+pxtPeFH0N5BoknXATlAVcgOVQMA+AifpkvmLeylv5bgmwxw8Ji7thYCHujBC/UOMpvySeGl6lyg4IY2gMkHtUmuA6P2rhoNR8d5gL32li8h5eUI3Q/7cJkoprP+B4wIc+ncIQTKDB9WOXhGV988b9+YOe5ZeZChn1yKdvm8JtZ+8t2CkLID6Fl7GWAxbG8K+8mwTq1x/tYdZGflgiUXzq5JWQ1+Dca6YBAInEHTJsO1SJ9B0dbHJZRXhHhXySggDmoDmP5cTwH5lfqTbdFyrv+PpJBpACEXZ+gCJU1VMPip83AkTmQWe5ltiCM8heMUOEZaZci/XIGXH79J4b1Y+H/qSG9+mt9mt53av5TSSVeY/GnYy/eMcQHCr6dnDts6488QIzcn2baeMZslKURz43k+8wHGgCBlDw0v8UOusS5aSpSHf7al9boGAouKiVzaLwKvMes1i4+fyKpEAHJ0bM/zqYKqKzPZE1AHoFcbwNPbtbj+UZ6t3eEMDpe/7KMI8WkzoP6v3HsLLBFcepVwY5WRcXaChpD14gn9TGsEZ0bmEflje30As8DpaPXBKSaIJRvDefuBYObm3LyE0xZIHLISqUeGb9w8q+U+B9vp2u7a/f4pVnHiDh62BgwFlbwJBjWsU0cZTTQv5z1ehuJKquZpkwf96VGm0u7+qLHr/UiRNweADpINFgvZEcLRqsfbS5P5HY8MWHkQlOplizpopgg2de4atKtfXC6q3gsFVWJv4wnokxa2urlgleUuxNE/kOjZhTBlIwE+PGQBwMz85kRDLVYZCCAi3PoO/0WEb4Wx3fH+Asune6YrMmGz4Hhk3wV1HTzE2+1j6xiDIszjmD0Uq95TVAqQWD6sBz+Sww4CJ2F8N5dhodvrPsfpmoEZdl/lDMtuXFXl2q1XdJfv1C3PltE80IVwbu9opvocY9C95dXRSMGPl96PrL0+eRpcxy0mlCBLD+fujSNbS2ME8uMId+2I494t4eProErDzvKMKRXQia4SxTp7uJcMCOlHkGEYDw7/osgX7l46ht0zQaw2TRKRknxz1Tl2dod8M4gNAWCePnuSLTt77AZfq9vyyzqY7+jDOmy9gVCazeaUAeLi7lUYW2F1SZ9Ekws59PralQhDPz/ZmFIPT2GXarZRQIIFIqFY4NWutN16IMm+eoCetMCZDxXf7HC+/AWGOzuYn85gilx/W2I3svyYF8mxOkOjADcnQIYImyI+l1umtILvaJdMf6lfRqLG1xT/Ditp6Hd8A4Zpzm1JJ7QDwS4qz9IRBJWP2qMPhw8OhMYTE3vPN3DtJ2VccndwRTQ0D8xNdgFDb+ST6vuzhGo4G08O+DOxF/mOW8f20u4MMtNDjZbmYRL1PY1Oi3tjEeRTKnm99Fz70/ldCHsbQfVGruhxpXQXWzMDVXFpKE/j4gpN65RRHonb7aOV15jvt81TWhcUZuST26YCKWpDpGZY/rXdxk7DXK0EE40fHYb8vxb8ib8khVuDQLdjii8D00nXjjwcmrPNUp89jeB3uQfsz6tyOzNJjR07ZBiNJcuZYsNDzUsyul5sYnUBohlR0xeWvkxA16uRB3bj40DPYQBWY8SdjRFbUZn4gByzWqLJabNGXLHiv/WWYibnG0r9kBw8ZbdfD+zhNB7Dk7Ytp2pb3LCI4M4ZF5/0+Eu1FGDGMm8q8qD4VDoaCeugHYlPTO7r7I+ZHd/of3Qwe+39ocG8peQBND5E0UQqJIbojYXHpxYBvyIBHpZ7pIhDKLumlUexBydHw5cZGMggaOAJXTeHPbZl9IMYWwejagHl717Il4D+OBSppoPgTDCw3ttXjX5Ixmr5ZKWwY7KDFQwEpdOlf1GIYSZP5LR4xbOoZuICvw9aNB2yd9iJuTLtZ7giQ30OZ8lcR6bN3MAEHr40CcKPngfpNNR9UineTzyawdTX8lQg/6EaPKA/YQncLUbZuN8366FkDD7CtGjYRBHZTTmawE9oI+SJRfrKeoIL6qunGO0V1a8rUZsdVEPYNmoRpQzAM84F5s8/MkxNvd1yPvjCuygxRZIUg3HMS3iEAASAcDs+q4fr5/7ecR5F2SiUAmLQmdDKfCJsAdv1Q7s0ymiXspSJxC1rlPDKdPHawr+/NCZ8lKa+zbPzNxdjY+svEFox4Edxd+XzU6RHIcm8Y9ITkD2Fb3+DDKI5H93cb+tppxdZuSTkRcEi4rPUjw2/zwi2QHxXaBQ3ScBQQOyDUAu+hctx4nFy33eF162RRP93R1PxBJ9Ba27Z7OewA51G+1UBjBeIWasPUvdDVkQbxHH9O6lUWA4n/8sXcDi99Dkr8D53B28ticmuAb8KihOCxg0hvH1TKkX7PUHhVAZ4ZY39dOIORb5X1EMRXJ4MtGkgCPQBmlEM4dl7XLK1AZJ+9m3L55y4kC0haT8CZEiITm6OxCCnua3wsIDwUPRgvR2picyepn3iZML0+VTNiolR9Q4EdCtcjRiySniFvWuqkf0ZOv9gZ3Jyat7fuGb95Fj3GZ++t7w+Bl8IcUKx9ycGlUtGut2lm29qf4go0WBptHzqG3c/y2KOOfQg/+iRgjzFmb/60uUIfNrlkUTGePN6JmiUIr4nCsB6PEnRtni7CHfVjmAQmhZTtP0qQ2q0aC8JmMNhAF7J4G3F2yPCBN+fVtluopRuWm6EZbvnxHV/m2/H5jOmAgzd6ZbvreaFLi7z4EqRGdHrZH7xyvE0Bw83a6/92cN/SNCoZhGKM0sdwzBBTift80WqlRxJqXewnvnpGI/cwZev9w5tQ1UNBnZlL7o2HjRanpWtDAWDxUjnlpgq5Cc+UoSWDKVI8alSZ3zSiL2QKljRthZM12dOcEuzxdRSNHOzPK3I2KPp8poXNQRLTRVJCsUiDCBXsrwepBihbg3i3WnC5SAeq8fQXa+zyh9QruGoNowAr0FDHHjN/ij+eAaDzk//w5D8tlmovKdUo/5AfFzgtYJr4iHvV/wmt6Q66Dv9mVgKoXqqaeklVUuZrKCAuJuArQztwCqAALBBV2WRthr0Qzfzn43goyV8VAJ5ege5bAjHRBLNsdOEf9HqKz2sfSUM4cy8nqSn2VcBJRr7KTghleg6LSlst1mYec3tOlm7kHt6S8oQzyQOzZA+wUG35mOKbHCbB8oXngLxFg+Et3X8VHvOzmCHynwJyiOIUWO62lyYmT1WS6m9FNZYrL9if1Mf4rC3P1fbWF5KzkIVyA6Krr+NpLDnLHSAbxMjNsRLUOcOVk6qV66pmxZe5fSXHRJnbiP/RGWqL5qcBUgIOJE7uN62rcSL+M6Tk7j8Wuq+1NtjOHbbZoUkr831oWbipTMfTw8ztkcEPthuh+sfilGtGSgza0yQIYI+C1Xj6IK8OOhhSnPz6/1A3X8BGj5jobPTE/ZW7yp+a7g+/tMQZrNwnpeaq+G8hylc+Y0VVkJbSafIzjk9oORTkg6wYclBN9oGodfge5+C5EUhQpPPUeIcQj9OV+kCPi68grbETRGdU908HVkUlBF3wGWQb0UAQBe/C0n7NmF9zqVugsTnmmRH+0NLXS4tNV9AynFWP5/9KCfSDLSFS/YhU+wXDZyKKdOEVB+QH4QyxuCMP3JnApMpROv3M2XOV71D2vC9b92MH6VUYbSoMoswOZ+w7A1YJs2Zgx/pUzzq9kkfGFAxQWHEwQRYp7HfF5nktHL7GGwqbbzWK2/IyfeTM2bDDV5kqukbVTOCE82D+dA/kALGpgfdKURFBhAmd9dLaxjyDZLyTvZZ4AmEn9ZhKVxwOPLsIekm7BCdwyn7QNFsZVlyEiYTSfaFh5QjbEBXhniHVefs0gmn8smPk0zmsRx8VWJEn+o3ktPdLxqWUSfE3ZTBVgBosCmbBR1lqIXxSwEcGzHGLig+raEVzmIJ8bdK4VHAkYt9P5JyW/ubK829wQAdyVFJXj+///YOrXxdWFBoHiA8IJ3cS8aVf+PnbpC76TtjoxXAFds5eJP7nPZLDrs9lJ/O6TOlb+LplOfyDoPXXQZo0Nxscs2hDvxSKtvKleRHPA8DPxC72d6JmdQLI26kbf6C7bCaO7P5uWreJr6QwMfk86/ImT7kjAYIeE3mbPHz2dVcKfgZKpBbzPVWVoAtz7bg0IQuyXWiDgELPl5mOkkLKtM5HwhvKbgr/lu1ScGCTZe658PbSRyNcE7aDKhfaA1PcrcPdekbs0mfVVAyOLXn9Xaes0OnoJyFX6rCj5Nxqg9mJtRbkbbrfFbHym5xnHFNg8ekda4a21IJF7zEc1yjwAgXcmC/7wixHpe83DRepRzOOanlt+lf/UKPcBoiww4j1JittaxJdLoskbEj+mJfoUOxl66zvabLvngSbhyfcURJgn37rs0zhsr0miBubD9ko8Pq6EL5mKc8a35674nZLEyODL5K4X3lkcyPFCrO9q4toUo5dtmMVZfqAOAl2O7kfVCfG2z4I4eNrO9SBCGC2O/wCTfbO4CMaB58fx2SuRlWdS0+QOqgHBy97Nn8FKgRkNuttOZs7I+UBrKoB5cXfwDLUdDqMhvvt9vZZkbgGbZHEcdz/f60aZZErMFjfNDr5/Jw99MneDqKj0KN1EWnTThvTCx36RSxnyOPePP11qLABoxgJ5kbDNyHf+uS5cidwg2KkPNV+clSqTzdDTEb2ICjZz/Lb3saQ/6ucfoWjh+OdYV3dlXxZFZZnXHNJoMgAaL//wpINX76A+7QLipp5T5BLh36uh/SfIAmA3gR7pQVvKi7anZ5KE17DAzX95MxVlxBeupWr732iMhqm41p50ZNkRotrAjV+EScrrGDSm7vb/bXmtAmx/Pi5nhHiKmRdpl1K3vxju6rtWK11LBY1oUo46X3ToTC+iTzkRPqX18Tk3emC0opgpgpOrzWmLtX4sXCi6F9O9MjYv3nbPQ/XK7sMAuJQzwCgGudeJVVlk3DtAr8l02BT9Nx/IHKpqxrwad711+aLHfKm4/kl/GBywSkIkuW6Dnc0HdNcw5CuxHjHe7c/ihwkykDleM7wZWt2WPrgjdFtIx8d98B7Hoou9r3LNq0xKqudoYbjt67jy3aXeI/E6Sufb7mO1iYLIrz3xtEYJXELxein8iAtQq7qqhb9IlSQXapJ0SAt0xu0wJHBhSRLRFs1//2Wzo/ICcFHFIL/E/E6t6dWp23FuotN1BAdNdaE1pL+AaQ4ccShmaGedUYDm0t2MPQTZlE4MZvxBQSCecOV+czMw/3uFoqfW0nKkLK0MXLFFSTrqpRHgmupYIfh5aMo17Qlgy0zk3AcZ9gjNnPBAm9YqSDx+PS5L8oH8uY8q6WJNUw4vwwAVzzrDxTibmlYtXFg6qH5x11dEvJBIH55258NcVzrn+fcb2ECqkIQiy5Xb/62SShKPYosCle7YBuUhXanPE51KTNZaoh4+2oLIoS86KVpdRAeUp1iUmgmpiB3+vZAF71qKnmlaUkgGlhzmGJmpDyN3hhxOtWYeKMRxu977XwlT+mEvKJVfbODUm3pkJlPqd4zOYdRghjFCh+FxGtaK1+jMgIvxCQLTlbkrhoCuvVPh9gtInouBYG/oAc1WgrE+ZdV3PV0JkdOc/sVf7w1GTLVeqA5euWs4+v40nK5JMwwruZfNOMc4JWc1jBT7GeOeOgdRAu6R/KEhst7ByLu908YiZbcolT3XNDPraeKyeTWGZQ1DxWGXdPKWmmvl5NwlMbT8KcZIoJRjtyrpg15yUsXk1sGnTTItAm4JMleZF9oZ/vBPYbKTeG3Rt6zm61EEtoNZje+tTzMFXlxhn64CAYb7VYBfiu7MRewgJBkItjwpaG1WJqvwF7Bjiqd1oqBkQdR0427klgSyCz3v9Lw7iHXbJ9sFpYmjZokTpzc2P941rOLipUlJjbUnrtCugLoAqtiCXWYC/do805A2x3lG16RQrZS16ISk0aZH6II7udvyKi6RzkvbNVzGDj5jK0s4ZEuG61GPKMd8Q2wuQbOpKSjPOTcXu8+KmJBeGtC5JZSF6DmTPhSHf/WR7sX4qfuERGV5nV664ugldjwou7gyQPzE7g5NmqpADGWuucQkQZSRwC3Ew1qY0FJ7jcmh6EmSgTAsy9vSTvv6cyPFGePYnTJe+kqVX2xykDK0Nrs0Rcd05uKiaqYkDKl8NfVP4TGXnens4qrnahEwtCu9OizW1UEg+47gkSLa6nZNswak0F3qi4NpE+ToFVca0KF/Agly/XLJw+YmGxYF7KPLJp+5WoJRe5/7Qor+1DkQrBJWYecyWYEWia+8Ypr3kHgwdV0gYwcuBlN/OPBPlwJTUHre8mQjgFC1msgQlTjodPowddITlt3zQnZYWFa661ptuovfbChzaXBWGlNd1wRHWVWi+518Nbn/rjkj0ful0XVV35diaJc/TYOtt/tkPMs81r7R4KtC8lm4RuSj5HcjXtgfkJCpoWD3FVytN1ezYQO/99udAaXi9GA3COiXbhrSL6IlWwMtlG4IUjjCUQpcfJpEJg7u1bg19pUiNoXMrLyxaDiL/h5FN8jW+0vGsiJXRXjyD0bPBQ+DYODltWccVWmI2+Bz7orRRAvN2H7Xv8xtU/DHzBZKkJODTmjuGZnVfz1RAGe423LHa6LzPbVd7cUepkwefza2hJB7H0ovTQiPooUOrYTnN3drhytLsJtmkNzvNi6uxaOF7FYWJq7KgWoPUD4BLqdwndye1RwiMHk+soqNZ1jKgLlB5EQ+yrcGMSX0jqXr1YNMITiSEN0YNHG3vk2C01mo1zdhk0bhjBno9q6YmIXw8ynG1UhzX+oPqwQtAHFtRr4kzTF22Jt9n3FPGb2Z3NrWKMXgQb4QN7XIiM3JSxndIpHSHW5ldSSDUqc5um+XdpvyuLUs5oqyWuztpNtXq6D9IHMyJ6VKAcPJCYu8b6SFrYe2eryBbm3bMODBgF3ILPwPtzSyA3TN2fcDBgkU6/I8esS2iC707rZAyoP+a3iw0zfqLvtrgIxo6sHPeAmg0OEQocJB7sPr4M6mRxJEvna4E1BG8kFZed5DiPDLPW8B+/9Jo4nJrKx7RQx7akLJGR4GAIliPLY4GXr1xsEhlJP7/5MU9f7TXhgon6IbU05i6vj/QgYKxybsn2jZ9Yc91N00twrjg+gCa6mOI4TFoYx0oWeXInk8kzSOgxv6I+Sc+vLuB7D3LVtDouqPKeSNi7w01EakQE5YKSBDLgKpD0ZKECf/8WUGiQzn4q50p07YrWAYol+XNO/lnqrvOl//obQ/zZ2jIYTt6PtwDFhpJU6u7ISWc2rNQ4et8x2GV/lENE4nDiBVgeQ3aN8P87gQ4nbhZJ0XpwB5Hj5vPWlRlnHIMy0Tqxe9a9vEwYUm0JH936H4QimNMZCx9lMw0VVFIHKtTqoyryH8waTRFlhgbtSzL97HwUP+nXtoZjh8A5/84pEeRCp5suCaC1i7+4b3TWmBVunfa7K5uUCgeiEY7s/GZRujKU9aRfRwSm8VrxFD6+Q0lOKhgVffNVBKcCUspjX2pjNmkSgUjvKRo8zbS/6uc0o8yb5vNwkyNzs0gCWsSl3OtrHQTiBT4oTzi3PREu/KAUDwpQrWr3I8c3Ljrw25s/tZR7SBsco0LDyKgnmattesxsVk3jzA3gnIqqV/Rbba1xuFCkfVjOmVtg8U1G2JY1y8ss066tIc9sh7pJ7umse8v8NdL9jxVOIhTYL037o8YR4bNWEJaH4wFCNqnMh3t9qOnCvvVUgRrNLQgk6ZzUegmP3dwIhBP9VVpwpxa7Y63yGu+p+FxGZho0ufuhIrBRnJOv3wBDCthKFEVElhtx6sYBndXmMm6RppinLKAj4FhMykeeL7GMrTmj54DwyuXt0DAfFSNdtRZy+JQwtDFAH7jdf5Ht+h1u63NNYQ/fWgZdckdOsvAprTqLnm3tk2DXmavpckyWr07ZJ2PSKX+h9BbFvlENd4P1ircUSIy2DGg7dyvrXOcGDBm3aJV6SRozyWqp7ctCXlW3xVbyhNyLnyFNOnxhrMee5O1BYtpd3sDBaBvGIVo92ADrKhZ5rNhB0vWqJFqpCNT5Dd3R/hu6MJHa9fx1LKsKcerqyQv1jh7j1LyYEqt7EzTKjJ6V4bz6lBExegug8zXUobjQ+XTLB56Szbm0UMym3FJZOhquifUTGL9F+jejfRV5f8cjCBG+CDjIId2Skt/iIX1ANBDqfczIpV+SPwGBgJx1magtIwGjF4nMMGpAjobH8s9+jXC4F6MKtW0txHrnK/9y5xyB48ZNyoQ6KjzXu1iVWrW6guOcGfafk66RFb3phDbwAs09H4rQxjSdtQpXzlyCrCAcnT46Ndp/4NeUep/KZFcaHPdxZuQlJUMgwtQBDUbjzoBjn8huNBkEVWvXO5tPPjh/gWgJi+LkTRn0l+XC9OsAeb9QNMqg6c+8I3hXvVskwAhvfY35yJxUr9vGQHHoHCc+UP8lK+maAePvcsbNmnxjrRScLmHA/RS5AwehDa0VSb6dCNdw2z2xuefpVWzGgiWR4IK3i3FF7A7z/YB7vl0spUHxeFJT0+0v3UEoRyxHJyCee04dQ7yT7fHp+aDaZHBCFwCm0TcWUxBNnefjg9y6tyzep0ispQwyU/qwVwqWWTJmfy7RJqe0advZsU6IqXwGIitMQBCCjL3Ny8IphDck98pM/ooLIjjM4yGu/qrkSbzAQfXf3GQZP16rH9DZ2kbat6xfw/wnrr+bhZb27xNyNZUojKH3ZMOAwvuj2SAry8NymAyuimQJ9Xm14xq0/f/ki0rfK2gyD6AOfVLN5i/DvBCGxbVH1AcYoKY5BwHVkcFO3iAL4kdW+sxhREyTmRMngrT+7FWGUCKVTqyrLCsBPs0ntMziaSMKcSyXBf7Y7Zn6+28vkUPTFp8Y9dg9/NKx2hNyYTetgqR79jp+5v288L42G/wvCMZjgXtowNFeTZai6qE7irhrfObOYvufWPl0dEJvuTXBCFZbv8Qh3IYyzO854m8kynXFTTsRAKrOzs1T/ISbcUztdT6wytBPj5V2t+MXJ0eQmwJRGvxIKScXoD2d8YCYZ1SyLhOusE1Cb7LRv9bHz74VXwL9RnaK9SOMHk+mstmK/T4mu6o2Xmxv3sr9YI5s3L2BCQORGKcVJ5hqSeroC7GaHEl+qP8UCR5cTK+Z+bT5EvODgfKYg4P8dGfOJKsViDsO2YyHFiu7zvKwuVnX1cr5g7jz4reW7Ze81JlZkWxCuJZbNCowSIt/X0UMWs8kcfaSPNRS3EjXu+ggE5T9d8I8spFVj2oKjKceaxRO4wD0j9jjzNGWAMj36QnaV8goIvJtqTok/I1q0TUqanzx+7Zk/3RLCtqFFitQHWBiMwsstyXW2tKVXDVwA4oFDcm6z2FJUu8j1i6wkzqqveoT6gmUrsgZkR3q93rH8DMPB20ZQHXcp2kTEsf63aue+zXhYG+692g5awxS2vHQBZ2rMApIp8mkyA3ZkOR4ljzqd0szH/bw/wAOXWG7blHFuCvMretaIdPx0/4ZkScN2V5CUvd1a6BaiR5EK4sf8TDQmGUygHOIjssHnmRqNoCGMCiT3EjNJ/JPeO1EX1/Cu6eQM8mK2jPZkqV4K8D+IR/2EGUgI3HhfqacVG4pw7t8rtGCGomPEnfy/4B8caKxZf4th5bS4flWvhj/Y87+U415PDGiG5cBwfanaj7C6b1ahvonJpNW1QDnYv/SNsg9Mj3usHPBm9vgiKSxOlEjNsluPaMNcU1e0mLvdGRUL9p0eylOzxuqld4ALmW+vDCjNUOIcN6S+7Z1GIcXsrBk1cl7JLqBMMBlxDJuDRAHXeVuz3EZbMQEZyTBaxhpIEylLxhA3f7CNeiRXc4YIiQl3Bvz0v1pRQ9cAeFD0kEC54V1klr7dbqXxEA9G+tbHDBXvHaRAdQVRyjwLvkxpnZyvXh0D+mGEERsnsuEAxJscNW4uD7xvZE5fgIwbP0jY5maCkzANavPWK2GC67me+Z91TYBqpr3tJ8z5eZ+sPMybI+X/wbFIXW2hrq1uEBn+y1z8jdkjHRAGZ0b0uyjtH4FrKhYCiXzLn+5AGZABntR+H0mTj6EpXPmYUdak1Uj4Y1CqcamZQ4uR2r3atBL41xS0rGJWjX09Q2zKJtdd0ypcj4ALFLkXJHc1RydRogro8cuv1t/0obMT2x5ek8EJbbqff2whrHQL8WL2vqZeF74JijS7YrMg180O4RD9JddEo/VZijwdKTei5mIBC9YnNwyOl4KHt5I1rbaL79gc0gUWRArJABr47CHO4sYh61VTRryD4IxFVsYKl8hDONmPev3Lg1mTJmh34HDmAnOOcjFf4XuLi4CqwzFGrwfIOpg3eMWNhv5RMyKjAO8USd/7SGhzUbQ/lVauZoKoTeb/tMGH8bJ4QLe9lS1Q79JIyKzI9hMSavzb39GAKMbnHNbN9Vr6pPb5rjnUTAacfWSJ1LRfD5YbsDNOlatKfaLLZpWueB7O9GXBx+7NLHPdmq2uoWkKJhQBH/hk+HDvLqzGUtzYaZeXlegtzZ3icBH1FrSKho5Hv0XbEKlSqodVBDMhjfgQeF06Q4QY4VL5Lr7kLiJxauT5kL9rd8jOWjiAVaA/jAA3ZYTLYjKbl6uJvQKoj1wk9AlvUVdDRibpZE5vTFESZfzFbvytKcNX/MS6Owp5LRaBrvqNm3S+8tPqkyBTNyCuxhppdcJmw+HXiDFWcJeRTonSvMw31Yq0rTZIFqxNIjVCH2TBQj85OUPTPV7NlymcndgS1qwTC7eXWwFD8WrBpnA4UpX+itT/9ZSjavehXGp3j+rbSMkuQ6Ev1YCneVzCHXtBLkhEF7ye4Jg7Wp0z7Zn1ZLgyxPL3Zx0jn10g3rrKL3BaHkong7+Z4CDAdPq2sCtY61SLXS1Vjr9vjrVja4Q1txknXqm+CWyducw9+LNIsRKXi1h8AitSyZTl0YS4CwgAX0Yet905gU9sIVoY11pc9KU3eJeJtIm1cchWtfUDlTo+9OGSJn8eKl9I7zB5PPh0x0i4XDcB+oz5vZTprAiN3iAI1i0eQxxYf39V9uIlsW/y3grcnOK4bZJG/YSLSFQquR+30kNUx1scM//TdvqzRfAAOSPIJjFx+y0wh8YAtHHEpG8bSsOz78c1UzfKsP5AAIAjkPby9o87JT4h0NpY7vv7no30t80V9VKSRQD2KBgNEWK0JRcXF5lN1IFOjI+jUEm/buWriTKgsVRdze/t3t9oEOfx2/WsRtIWZJBwQUbOrGGGyY+UQWcRGx8qjce21zp9ed/i01FZ2WZhC1RcsKQT8p1irVORdMaaloWogV0DomV02R6Ao88YHgKqOekSiKLt5TR+IMcctlvFS8TEbcIjEOO/1Dzhh2jjYzCaJ8pCS8z8smebHGXuSujpIjGOY1fyT0oWacNm9brD1MVg4eInU/I1m4aDLX7vrrccgH7rqwabQl/GgtFZZsLlBcgSdIOjN2BBWSNGgqtc9p4hLJd3GI/IfdgmnC3qWTvVX+gtsSN1fqn59yXjJD4Ii+1q+TdWWfx99pzwqaw+db/rmUV8zxu5OtxJ7VWZzgrZjEWzYaLtxxB2bWbB7/FlWlAxqpsDMhWjzEkwxqR3E6huoOB/HQFhmivKU+oB2yOIM0DbLaU90h1tkurQje0aw/EkopYpCSMxg9VazrusuccHseGW/gp4oQ3wDM5Y/zQxfRc7xetbrSzF65dzJgcFfR4s2aq9CvmZVlkNWQpoe99FN+HTGLK7+t49V8NvGyMTd8KszbG4eXlaHznpDj3Ls5SC79akFzucs/ZmRSHGVaYLsx9eZ1BwnkauA9pc9X5TpwulU+IIa8gAImImpGP9K/1SjKSfOqbQBzGcS5c3YMGa63g6N2PpQx8/ylP4pyh6uG9lpw2MVhqudGyn4S8UsRsunHjoaqoQol19MgBtLWW9rvU+Hy1chrXkeYCOrXpte0pL/ODgYD9CQ7tqjxzFicZFZekoYRultTnQ3shxLxSeKxFKddORKMj8xmlRtLg68PUobGzzlM6kExl79VgNCEZggAdzhfbdZuuFoBiLvl+nITFqV30RgAXa9tcvwY952pv/25Fh8GSXgmBNKv+U6zhjmPMJirzhdQDGRrqXW5WjUsoiMrugiBrsfN5gYazRcyqdo+2U4G/YIXl5gWCdn8pqQn8db9aoyMw29gQ0xUIeFNj25XHZ5Hs8vvvEcojvAaOZLPxT1ixGlOfbWv7MiH/YlBdo7c666LFuvA3YGBMV5rp3+Cz5ZDv09ynIpGfn8foXuN0k5id+dnu2dPLYq+7JdxFmIaLTiIOXJQcaBVR2RXHnqnkvq0Sl4+N3imc+GGWZkG7urtYtDCgtVJJ1kyGoecvVRzNADV8JKygSKDRY+M9+edk6uq8zgy6p7cVqvdLAm3FJRaVK1TJyLpWOJCwjKTLRcdT2L/HgNC8iFkgzWR4BRehBeRWVhqm8zDm56q2lSuOjfKd6m+jZViG2cLMZL0b2Ik0g1I/JlJvnIeob74tE2+ZwKamKPYyTKRW2aTHuRmJCdK1ESCUAKkMbRLa+uGjM4TfIRDJwC3c/cSiLnojTWj5LWw==\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" finds","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"/re","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/re"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/re"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"packages","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" repeated","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" card","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" patterns","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" token","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"db2RfIwt/EgN7Qw7W7gyu50pUYtgaOPhhUk8C6UJ85BEFwjrBoRLCVYHwFKlrlvbd5LLU3crSpUtkMwezu8tC7OELMqMgl4f77cvqql9yCqIwP6kC/ULuZhqeeBcgqwGa+QRh1QuTT5atClVUk3/pBNRXF+tCIKqQtKRNXb/R2HRcHn+MkmmITmLw/csoSsA0bBdsbUuf6F9FtUM8cbVWOQMbo6lL6nevnqWOUmKXqQeuwWCaNH+mbmgu2gGruIZAiO7lS1NmcMx1HblJj5rwhvxfbAnWqUL8bc3tPYQBT2Jwtaz6GTkGzXioY1yK19rjyXZQcXI3+gomGjZvCSemmSXjekS4II3Cp1ZFw+Ub2QTfk5ih94R5nWUxIdC5lbXyVQGkEVlhh4mfulIkwuSvKAAhzfuFeP0z9GM8ghItDrRj0QCERiukneezpU6gh3R25N4pD6aySB3LKRdMMWTIermlT96vRnAGl7zREFzeoAvxZ/Cukt1zj1hClPbz0ZIBuz0vHzF+Uaonbr6PpO9ZNSvUmDl8VBiqDC010js6ywQuRUABc8KmUVGuKXiPoirKTNlk8gdl5mis5hkPfAIBlKYgLcdMa1Mpypi9VAeVgsGxGCKCmdrxL1FwTVwz+mefenlsta1hSpybw5u8yRkdUFyOI2Aeahim5oXNDeIJjxL3xV7CGxvlq/2/ZNte7KZAfSWg8CtjJumFx+G3EaQlBLL9Py+ereiwEy+9cWZX5AhsaPrICf+gCxaODregS8wPK7CUpvtjG6hcCN57fmrVSzLEkDJm6jCADghImUrf8U6khZeILQub4NGUjUa7/KWkNUkAVIZ0ibZUMs4TVMD5un9AFNcqqnP8zoP1Li8RebzbRtTgXoa/BQogHCDAf7jFBzz/YFxphIEtWhxnwlJEfntUkvU/JYqwS+5mEg8TpRtsWZ+IaHsLtaLSwwZ71K+rAwOKGSoDcbPvUzR119j3vaVhljpM0GTLfPt07kJqHEN7Sp2Q+8PgG8MecrdTVAYYNFKYQAYuU10WZ9KhzzOKGu9Ziyd7pexl3ZDsDFnm9jS6YYB/m2tT+JybP3MTPNv2GTNwcf0A0hpc0iduC9z2JNeytRqxmLQlX2myRHaEYt8Z+Uen8GJPhZ6dosxCa2f64dv7SEYJrW2+d+AfYX2mckOP3PbQnLqJaEnKUMtkgDLLrkz3keWc2MsgjgvEE47zpWliDu1nshlBOlH6fVd0pMbyd6Ncv/h7NBAzPd0uJ6HIWBhX5vyxSBvGP4nM86xoYyfsADF68NEYhGBCE5pCDmc3rH4QrVVeWYkUHXNGIdYDsxkvt4i8jt/D3Y2cK5KMmTRtnmdU2zp0N5LUTKOHPvuFMPbaEGA8lSEEol+F24RkFFVywB8k1k40pCrmKGHN748U4iJEHXKLDBgFBRngMd2HBfK9W9Q9sPAAV8kd3o0h5pHqdevHFdA9a+UDzeALwwtz08NRx5Rfv9ZP6Nym6xJdQ6qmz64meaXSKtyydUBJYrWrCVmd9KV5MdACbLL7Y2zGGYUvgunLQkU5Ixgd2JupAt/TSfXrp2fkCaeIwlT+4Yx8LSDuNx/q6/CiUbniZgWbZOkRVhVFYS929EI5kYif1/cz4YKwN9kLTsDJQFryJQl6OEuqEfNscxeQtR93hVIqEThhuORd/tsJwXoCOh21rZyB+fi1MVqJ0G/MOSpuB0smIOk8DY5o9gurCO8KGDPNjp+lT+zpz12j20dzCtrCsdohBDL48dFgAzPxkYxI3b9RejL/ilrQ7pdPYOLstUrIlYK2q5bxOadd2SsT1935UKDUlIWr5zV63T88N5qEdsJ0EdtM/Ke/1SwvKduyasy+fbNNa17cMtRIF4t58XgriqKT1Te1ljUOb1Lq/yHGL0zYn418Kyrob5sAgTMrFGgr9NaNj7ESlhhQqJntiwBoGCgGZI432Q7Za4vbzyoFodKTK6CgC2gZWofK2zFb1QWSc5vXdQx5Xk0xvt5eQun5mWOQ7wEgljHYIVfo+NSQhnS2NiR/U3ae9Cwmi/NdG3gJpvK3fLydaKAzED/MF+nvG4xcCDxXRE7suzSObhiKrfYJhbahW8WWkXLHf9/+m0Be+nVA+kgOzq63UBVleN1c5Et02zecEnc8jA6vAsshyD6+O3WVbOxxqAo4l8P5udkH1EIFfPr1iXgEWHif686shj1MiHBcqiq5+vhn08A6VHbZ3P2B1O9T46jwvWvSQabZ7YvQ030gMmahCcgdkWLkYtn1ZPBwqpxs0J02lBG1An1kj+kUyOxu3+41yFc4UhTJ+EuYqY973iROZDN56R7hWefrxX0G/0WpkHpQUtstq0+YPNee3stS+8y9pgHqsZNdfCCJYib1IPrq0i5LsK114bWmLBQEZ1tVPHE8vZzratOTfyJg9TX3CJi7Afk2YHoHMjCzaCyOYnH3ym2vaRdzOwC2S2I06upCM+HQAqmaRMxqF/BLTeUsL+HkT52Bxq3UnZGQ1SWUDdya6IA0u2p5TOu5BFs4/A4sc7AG/Q1BWEIXbmskVWlgk6XJpHU9T+jO3PxPsTWvQGRtS8O2X7dBMCbyY99bUog+C5ljzSbPIBuIBqZKZJy/98dw4Lq5ZCBisGQi7ho4YX9svuKaCOs+Rmh2wwYYq6Fd+xH63B8DY3VsJr7Vw0qn+qUyvIzUU/S80dplg02jdY4rNM3TqFUzy1NPK2jYIy8ZIiddmZR/TlQW7KYszpa2PMPVxFrUbW3wiqCh1ffhM6JfRr22yW0L05uj871nZX2zUEkccgDC4KXN9AQ0V/WKP1of1ulo92ldHbUk1jhkN3to852uH9JkpTHniMeR/UVnaZjpalvIcexXENYYhuUVV4/gpMMAH+CYQvINqnTMcONVNR6Hrlob+ojMBK6poeGo/r5q6MnDnOaVfeFPKspRT2wA5h//oDIr3kWg+kfv9/nv+9ExN0AF5ohBI4xRrUEoQ1r/GoSn5qLYXCb+Mta3YKsmWPsMDaQrKRGz2YPA+XmByUiZW3wgpz+o7lJa4aWqenTpabgHFpzlMwlZi9/sCGPiAEKelI87jBDgMB6bSFOAhatWKHdRQecd2MNwS41st8l7WT9GhOK5LNBvqxLBeUd+5PYFIS7wYXa/j//5PFdt8/OjBx/30b4aI5u1GC1y6RKwac4HUXLuiAPW3bsD10/PHKFQ1Vo+wMNyA5yy1P6geHZSxphHGlGgXOQKhXou8rczI5PFetekzJBrVPRIZwM0kEMDz0xsbNDwmAIlwMXvyszkukS7qdJ8dRX36hWonMGbJ4xJ9sqCzeQoznytO/TkmUa0MRtCM+Zul7X+Y0i6In3qsntz39648SSjowzS+0gaXzV1ZJVwoboSefVVNp/rODgByzJTTv4+MtV6rJUrvolFH9ORQPhckIDUNFE9wiWMSENYEs67vTmFBcXIwdVRaPf2A68IhG4IPNqZb5FbkGlKdzkltOSJ/dSZOYhOeEA68dCNLmsAvu7xzOAR8reMJM5zUStVWGBuMzDkjYBm4hnANXZ6BXfHDXNMC+YZ+dFvRZXcRwra1IEcIP2DanKa/NVGn7R8iy3Qv7qHJhPZtdU+rq95KEGkZBLWfrZKwVlMtZ37yM3XF40OxGi8moNJ30dovwNdDLSVtI0ekW5i1yGXZD9bXutYm8TZJBoXo57zdVcZiRQFaMYVF2jV+HWJEzx+FB/tUwhZp0jBSiAk2pr7HGbNmV3sxPh+VIVP+Nxj3ztqbHXY0/wVcWChp3OYWuGfKhaqymkFTk3ULWCyugix1BiGRxp4+V0HVAgI14X45u4F3HSGeSzAkf+F/vuQn1NDDU4F6/qc3fgpsEUYjDXvftZIttL6XSmugIgWXmo6RSIuhXs5pOisBDJ3fc1jNs87o/6TVrDY3wlrMiIjyIt7fec3b17NtutdIJVSsjd0/mfrbMzzvnMyKK6Q34lxoLWwKIejYikfPzwSX96jDfx5Uz8WTq9nBV4TUQpb6V6Dd1xnmDnDT5qwjIpZLHwivnaVg7v/2EisLe4lx0622aTAVRFjvcI8AqqWPiSVImj80UIkcOEdJGkVvjcoC4Qb/lzMZ84SCBBeCilYy8KpXJsK9Cu1tsb3dGBR2GZ93T0Alomumjth8xMZJF8CRBsQVPEuZVwzQjw3dvleELz2cT2PtQGLA1+t+QiElEBRYrW7Bp+jOuNu0blzJbiFpj3ODT7UrXIal92iWflN0N5AakzouOuQaYoFEtJTP3Lc2HDLfWJkPI4NrVMFxD3OV08Mbo3jPNMumy3VmjcNOGtfWElY7/KrlHN6yfB43NBaYMqbArIOdnmkANMgkCxoV2xbCwz2xC9ksKx+EtKXHmBp4gs2vxlAzBIrsOV5wjVEIIwavrTvlBmnr7vENLak7usnBLPSIXYrkLOYlpY+7J1Qr0Uc2N6E+mtEtqyb/nDAyyWluOHvOPN4ZfsPByhye8RD3MjoZhPsfRYY8mvZFpDR+cBXwkA3MDDTe1ouSfG4xYvg1b0RTYZ2UEIZtBWXSWGysSRQLGbBOU6fb9nl5WkVKjPCfbrckOd7nW05FHKCCeS3cUh8ILs4osr594ti/yb2gOm5wFVeFltpvu/Ojp7Fa6jAPSDIehV2aRdSI92YQEaagosCrVRDg9GCznZ4c7W1n93oASyIHOyD8n3XF2oAXk84FOVbQvklpKPt2ASRmlY/p8Gb41rxC/ZEDLxpx3cZHOUzr1Es7c2jG2OKTFFauAebhflGbZOSBryUDamjS5Twu/NvF077a0vJ5pyZBorBWYS5Rb1AjXuCE0JCa4TQHy542tlll35tB1eW9FC6Px4Qernh0Kkq+hJ3m3uWe867VaBcFfsWAaUFjtF2QEpFuZIswPveQIeYCcVL4CCZZqctMoeOXAgdM5KLuGV9Xp0r/aVt/T5bFwBMIjPVFtSr1kT7nvip44jV0/SmqmgWtVupfPqyNJqA7EVqpjegNWcJ/R2zCg7/npXSsc4cGQU47cVz9sq8qQAQmZwRSg4Ow0vLfQNzQwNpjEYu1F0ZwQFloW9O56dvdg5EWHsJL0FrvD2C7rgeX609NWSNSD94VJ5jxZrIw4IFmjARpp5oaCrxv0bHCygTtm1YemZij6KqyAKW6o1he1TGACJB0OMFVRFoDdxaYFKDIYCazQJY66nf/MhrBuO4HsgceaSqlO5Q/P1XbcUbzx4wc+RoE+S6qw3DgzBiUBzoMwgxjuT/k3lyOTShqvdwi9AgXa/IS5sfOC9LXQf3N5bvhNOE+O4Qd4M25YKz2gXm9h4jnlMwZkVFujrnQO4f36AMyY3aXe7/ulaY54RD65G7TiXwsMH0YQ2wj+gaGgEzzLjXCkyQt90ZuCqC8E9c+XqXsXikAHYWDM4Ga4YkcXsKkPfre2IEHMpoI3thgHI3PBIxxCQ/UmoMJids/K9+jdfCfzdNatyv/5xpdHqX49b506xEmFMSROas89a8/96r1KZv7sbvSZmmnrfDcmbpwmzZV89O2eLXAtdYgqp0jHlaYVUat80gm7Av9CNdHg+v/ondgsdTiFWXig9mSUGtGenPMs4aG892EC72CvbCyHW8ID07iQHWruRr5iB+l93qw2YHi3i1qIhKIfHIyh2WpHHkFIFpU6lZ8lz7xAuTK0y00bsjq7nJ+iT3hvcIQRo0PpLuF+SeUnrU/d5mGQRm2bXvg3qlumacoRn5LWow0PE8gWFOM0hLJGBE4nPgLL7KFtTKq5HSRy1FFLGtANFNBDy17Ct4lUrLi9CFIxDJufuPDef28FgZjG5Q0wEqejLYiHdTvoBbvxBB5/P6xnpTnidLiyQMRlLRbYUM+/zDnO+gMb9/2ysEdfRbJ8l1Np9Hh8uyxd7OWOqnUpTW1vbP1gybmbvApE/jiHJcvUC+EdwGxc+abBxXInQMWg5z1C5CoDSi3qRONpBSX/Cwloi4xeOtEjENpVcbEqJ+5txEsT5k41x7X+IOXgxN2S4p827xvWJWWmEdE5K1J/LMg6VdAUcoM4omw+jhsefzbbXseAoEU8goxTMMz0r3VXiEvnYQr9uAgGqDozv1Jdow+MAy6+9XKm/fqLEfHEjpLLXkpNJAuaGim5B2pEIFtQ7mVlnZcdgPbkzj1/1sicqpajorUMPUuP6i81rHY/+HSoTfYAleuZNearLXyAJ7x1c6Yhh0vD+TF4PEw+cxlnFRwi6QuxA4LPv4EbuQYM5eN7WeGGulUvQNiO6YLvYHigKpPQ7FiVH0AHbZXR7jzOPgsSnuDsCJJ9wy+dF7DPdg6g6Id6H93qXWTXyzIhGIp1NoMKXoDixBmdwa7iU+etDqVQtkwyE9e/8MIN5uBIvFcZ70SpV7QWDteqz7Wgs7aAv2om62WPKctFPo0FWizUz6kjddbGD7KRzicxjAc0G/2H1tfbwIBvtUXNmYb6Lz9Gg1fYjCrgiBlkDYKNFlysjn4jrtuMlXnbDg1Rs2b65p8AbeDSqDBGsMjd/UXm3KfjKZJaXtX9uzz7CPRC+W662mMgC6AUGPKZL/dtwTUhR1xAkzkSapUNKupE/f9IcPKWLMQ7ZHOlJxvzXvTF8Jjqt9ecB7cDuO4h4vs8glGg9QUk6MIx5SRMPKn6FKEa9zgDpq+2+iUla1pn/MQcHcfR9tYB2rQes2ahuLN3vrS8cRaYVG49D8DLBdn7b0LJCrbMYhMJtIEUrJOVVhXUjRCzjvZuWj7eh3tvfHL/ixL4BA+rDLG2w0cu98bvXnulZCEgou1iQPBx6YR+bLmS6gYzcENwjaskixqqMHzPh/QljCOhcT3riJubfd1QVunyi2705MC7Q4M4g6Lt8SxCCrkj2iuxD/jtsy3t/PWlyB+sVTmOjEUp1Z4buqx8NlhjRgNXM37oGhCFEglPbsqs6vXX87zm60oj4CSNRX2xrBoDzVagwUJwKHaqjr6/N4CS5BNK61dty/u1sabimbO7am/+yuKR+r5TSO/l4MyVll9YEhuGlcMSvSV37sJZ2pgIyQviWpcD/wFRKqQeENJIyS8MHL8V24Z8L/QEAJSjFTk1IgxKKC2IexSl3BBE4d8Cl+sHU86uNHfXy3pffN9IHflt4uEMC2yRJzQT1Shc5ofm7ir5+3UOtq+ipeHIl+p16MS7tvX4/P4hOsexDLvUzDaC6/B4g7MXv7BJn+i2Y8FfqcJpQ//pKMqDqECDb9SCwT9o/qfOm9TCdnsZwEPnNRXZiL8JCB/KJy5DgnLr+pxtPeFH0N5BoknXATlAVcgOVQMA+AifpkvmLeylv5bgmwxw8Ji7thYCHujBC/UOMpvySeGl6lyg4IY2gMkHtUmuA6P2rhoNR8d5gL32li8h5eUI3Q/7cJkoprP+B4wIc+ncIQTKDB9WOXhGV988b9+YOe5ZeZChn1yKdvm8JtZ+8t2CkLID6Fl7GWAxbG8K+8mwTq1x/tYdZGflgiUXzq5JWQ1+Dca6YBAInEHTJsO1SJ9B0dbHJZRXhHhXySggDmoDmP5cTwH5lfqTbdFyrv+PpJBpACEXZ+gCJU1VMPip83AkTmQWe5ltiCM8heMUOEZaZci/XIGXH79J4b1Y+H/qSG9+mt9mt53av5TSSVeY/GnYy/eMcQHCr6dnDts6488QIzcn2baeMZslKURz43k+8wHGgCBlDw0v8UOusS5aSpSHf7al9boGAouKiVzaLwKvMes1i4+fyKpEAHJ0bM/zqYKqKzPZE1AHoFcbwNPbtbj+UZ6t3eEMDpe/7KMI8WkzoP6v3HsLLBFcepVwY5WRcXaChpD14gn9TGsEZ0bmEflje30As8DpaPXBKSaIJRvDefuBYObm3LyE0xZIHLISqUeGb9w8q+U+B9vp2u7a/f4pVnHiDh62BgwFlbwJBjWsU0cZTTQv5z1ehuJKquZpkwf96VGm0u7+qLHr/UiRNweADpINFgvZEcLRqsfbS5P5HY8MWHkQlOplizpopgg2de4atKtfXC6q3gsFVWJv4wnokxa2urlgleUuxNE/kOjZhTBlIwE+PGQBwMz85kRDLVYZCCAi3PoO/0WEb4Wx3fH+Asune6YrMmGz4Hhk3wV1HTzE2+1j6xiDIszjmD0Uq95TVAqQWD6sBz+Sww4CJ2F8N5dhodvrPsfpmoEZdl/lDMtuXFXl2q1XdJfv1C3PltE80IVwbu9opvocY9C95dXRSMGPl96PrL0+eRpcxy0mlCBLD+fujSNbS2ME8uMId+2I494t4eProErDzvKMKRXQia4SxTp7uJcMCOlHkGEYDw7/osgX7l46ht0zQaw2TRKRknxz1Tl2dod8M4gNAWCePnuSLTt77AZfq9vyyzqY7+jDOmy9gVCazeaUAeLi7lUYW2F1SZ9Ekws59PralQhDPz/ZmFIPT2GXarZRQIIFIqFY4NWutN16IMm+eoCetMCZDxXf7HC+/AWGOzuYn85gilx/W2I3svyYF8mxOkOjADcnQIYImyI+l1umtILvaJdMf6lfRqLG1xT/Ditp6Hd8A4Zpzm1JJ7QDwS4qz9IRBJWP2qMPhw8OhMYTE3vPN3DtJ2VccndwRTQ0D8xNdgFDb+ST6vuzhGo4G08O+DOxF/mOW8f20u4MMtNDjZbmYRL1PY1Oi3tjEeRTKnm99Fz70/ldCHsbQfVGruhxpXQXWzMDVXFpKE/j4gpN65RRHonb7aOV15jvt81TWhcUZuST26YCKWpDpGZY/rXdxk7DXK0EE40fHYb8vxb8ib8khVuDQLdjii8D00nXjjwcmrPNUp89jeB3uQfsz6tyOzNJjR07ZBiNJcuZYsNDzUsyul5sYnUBohlR0xeWvkxA16uRB3bj40DPYQBWY8SdjRFbUZn4gByzWqLJabNGXLHiv/WWYibnG0r9kBw8ZbdfD+zhNB7Dk7Ytp2pb3LCI4M4ZF5/0+Eu1FGDGMm8q8qD4VDoaCeugHYlPTO7r7I+ZHd/of3Qwe+39ocG8peQBND5E0UQqJIbojYXHpxYBvyIBHpZ7pIhDKLumlUexBydHw5cZGMggaOAJXTeHPbZl9IMYWwejagHl717Il4D+OBSppoPgTDCw3ttXjX5Ixmr5ZKWwY7KDFQwEpdOlf1GIYSZP5LR4xbOoZuICvw9aNB2yd9iJuTLtZ7giQ30OZ8lcR6bN3MAEHr40CcKPngfpNNR9UineTzyawdTX8lQg/6EaPKA/YQncLUbZuN8366FkDD7CtGjYRBHZTTmawE9oI+SJRfrKeoIL6qunGO0V1a8rUZsdVEPYNmoRpQzAM84F5s8/MkxNvd1yPvjCuygxRZIUg3HMS3iEAASAcDs+q4fr5/7ecR5F2SiUAmLQmdDKfCJsAdv1Q7s0ymiXspSJxC1rlPDKdPHawr+/NCZ8lKa+zbPzNxdjY+svEFox4Edxd+XzU6RHIcm8Y9ITkD2Fb3+DDKI5H93cb+tppxdZuSTkRcEi4rPUjw2/zwi2QHxXaBQ3ScBQQOyDUAu+hctx4nFy33eF162RRP93R1PxBJ9Ba27Z7OewA51G+1UBjBeIWasPUvdDVkQbxHH9O6lUWA4n/8sXcDi99Dkr8D53B28ticmuAb8KihOCxg0hvH1TKkX7PUHhVAZ4ZY39dOIORb5X1EMRXJ4MtGkgCPQBmlEM4dl7XLK1AZJ+9m3L55y4kC0haT8CZEiITm6OxCCnua3wsIDwUPRgvR2picyepn3iZML0+VTNiolR9Q4EdCtcjRiySniFvWuqkf0ZOv9gZ3Jyat7fuGb95Fj3GZ++t7w+Bl8IcUKx9ycGlUtGut2lm29qf4go0WBptHzqG3c/y2KOOfQg/+iRgjzFmb/60uUIfNrlkUTGePN6JmiUIr4nCsB6PEnRtni7CHfVjmAQmhZTtP0qQ2q0aC8JmMNhAF7J4G3F2yPCBN+fVtluopRuWm6EZbvnxHV/m2/H5jOmAgzd6ZbvreaFLi7z4EqRGdHrZH7xyvE0Bw83a6/92cN/SNCoZhGKM0sdwzBBTift80WqlRxJqXewnvnpGI/cwZev9w5tQ1UNBnZlL7o2HjRanpWtDAWDxUjnlpgq5Cc+UoSWDKVI8alSZ3zSiL2QKljRthZM12dOcEuzxdRSNHOzPK3I2KPp8poXNQRLTRVJCsUiDCBXsrwepBihbg3i3WnC5SAeq8fQXa+zyh9QruGoNowAr0FDHHjN/ij+eAaDzk//w5D8tlmovKdUo/5AfFzgtYJr4iHvV/wmt6Q66Dv9mVgKoXqqaeklVUuZrKCAuJuArQztwCqAALBBV2WRthr0Qzfzn43goyV8VAJ5ege5bAjHRBLNsdOEf9HqKz2sfSUM4cy8nqSn2VcBJRr7KTghleg6LSlst1mYec3tOlm7kHt6S8oQzyQOzZA+wUG35mOKbHCbB8oXngLxFg+Et3X8VHvOzmCHynwJyiOIUWO62lyYmT1WS6m9FNZYrL9if1Mf4rC3P1fbWF5KzkIVyA6Krr+NpLDnLHSAbxMjNsRLUOcOVk6qV66pmxZe5fSXHRJnbiP/RGWqL5qcBUgIOJE7uN62rcSL+M6Tk7j8Wuq+1NtjOHbbZoUkr831oWbipTMfTw8ztkcEPthuh+sfilGtGSgza0yQIYI+C1Xj6IK8OOhhSnPz6/1A3X8BGj5jobPTE/ZW7yp+a7g+/tMQZrNwnpeaq+G8hylc+Y0VVkJbSafIzjk9oORTkg6wYclBN9oGodfge5+C5EUhQpPPUeIcQj9OV+kCPi68grbETRGdU908HVkUlBF3wGWQb0UAQBe/C0n7NmF9zqVugsTnmmRH+0NLXS4tNV9AynFWP5/9KCfSDLSFS/YhU+wXDZyKKdOEVB+QH4QyxuCMP3JnApMpROv3M2XOV71D2vC9b92MH6VUYbSoMoswOZ+w7A1YJs2Zgx/pUzzq9kkfGFAxQWHEwQRYp7HfF5nktHL7GGwqbbzWK2/IyfeTM2bDDV5kqukbVTOCE82D+dA/kALGpgfdKURFBhAmd9dLaxjyDZLyTvZZ4AmEn9ZhKVxwOPLsIekm7BCdwyn7QNFsZVlyEiYTSfaFh5QjbEBXhniHVefs0gmn8smPk0zmsRx8VWJEn+o3ktPdLxqWUSfE3ZTBVgBosCmbBR1lqIXxSwEcGzHGLig+raEVzmIJ8bdK4VHAkYt9P5JyW/ubK829wQAdyVFJXj+///YOrXxdWFBoHiA8IJ3cS8aVf+PnbpC76TtjoxXAFds5eJP7nPZLDrs9lJ/O6TOlb+LplOfyDoPXXQZo0Nxscs2hDvxSKtvKleRHPA8DPxC72d6JmdQLI26kbf6C7bCaO7P5uWreJr6QwMfk86/ImT7kjAYIeE3mbPHz2dVcKfgZKpBbzPVWVoAtz7bg0IQuyXWiDgELPl5mOkkLKtM5HwhvKbgr/lu1ScGCTZe658PbSRyNcE7aDKhfaA1PcrcPdekbs0mfVVAyOLXn9Xaes0OnoJyFX6rCj5Nxqg9mJtRbkbbrfFbHym5xnHFNg8ekda4a21IJF7zEc1yjwAgXcmC/7wixHpe83DRepRzOOanlt+lf/UKPcBoiww4j1JittaxJdLoskbEj+mJfoUOxl66zvabLvngSbhyfcURJgn37rs0zhsr0miBubD9ko8Pq6EL5mKc8a35674nZLEyODL5K4X3lkcyPFCrO9q4toUo5dtmMVZfqAOAl2O7kfVCfG2z4I4eNrO9SBCGC2O/wCTfbO4CMaB58fx2SuRlWdS0+QOqgHBy97Nn8FKgRkNuttOZs7I+UBrKoB5cXfwDLUdDqMhvvt9vZZkbgGbZHEcdz/f60aZZErMFjfNDr5/Jw99MneDqKj0KN1EWnTThvTCx36RSxnyOPePP11qLABoxgJ5kbDNyHf+uS5cidwg2KkPNV+clSqTzdDTEb2ICjZz/Lb3saQ/6ucfoWjh+OdYV3dlXxZFZZnXHNJoMgAaL//wpINX76A+7QLipp5T5BLh36uh/SfIAmA3gR7pQVvKi7anZ5KE17DAzX95MxVlxBeupWr732iMhqm41p50ZNkRotrAjV+EScrrGDSm7vb/bXmtAmx/Pi5nhHiKmRdpl1K3vxju6rtWK11LBY1oUo46X3ToTC+iTzkRPqX18Tk3emC0opgpgpOrzWmLtX4sXCi6F9O9MjYv3nbPQ/XK7sMAuJQzwCgGudeJVVlk3DtAr8l02BT9Nx/IHKpqxrwad711+aLHfKm4/kl/GBywSkIkuW6Dnc0HdNcw5CuxHjHe7c/ihwkykDleM7wZWt2WPrgjdFtIx8d98B7Hoou9r3LNq0xKqudoYbjt67jy3aXeI/E6Sufb7mO1iYLIrz3xtEYJXELxein8iAtQq7qqhb9IlSQXapJ0SAt0xu0wJHBhSRLRFs1//2Wzo/ICcFHFIL/E/E6t6dWp23FuotN1BAdNdaE1pL+AaQ4ccShmaGedUYDm0t2MPQTZlE4MZvxBQSCecOV+czMw/3uFoqfW0nKkLK0MXLFFSTrqpRHgmupYIfh5aMo17Qlgy0zk3AcZ9gjNnPBAm9YqSDx+PS5L8oH8uY8q6WJNUw4vwwAVzzrDxTibmlYtXFg6qH5x11dEvJBIH55258NcVzrn+fcb2ECqkIQiy5Xb/62SShKPYosCle7YBuUhXanPE51KTNZaoh4+2oLIoS86KVpdRAeUp1iUmgmpiB3+vZAF71qKnmlaUkgGlhzmGJmpDyN3hhxOtWYeKMRxu977XwlT+mEvKJVfbODUm3pkJlPqd4zOYdRghjFCh+FxGtaK1+jMgIvxCQLTlbkrhoCuvVPh9gtInouBYG/oAc1WgrE+ZdV3PV0JkdOc/sVf7w1GTLVeqA5euWs4+v40nK5JMwwruZfNOMc4JWc1jBT7GeOeOgdRAu6R/KEhst7ByLu908YiZbcolT3XNDPraeKyeTWGZQ1DxWGXdPKWmmvl5NwlMbT8KcZIoJRjtyrpg15yUsXk1sGnTTItAm4JMleZF9oZ/vBPYbKTeG3Rt6zm61EEtoNZje+tTzMFXlxhn64CAYb7VYBfiu7MRewgJBkItjwpaG1WJqvwF7Bjiqd1oqBkQdR0427klgSyCz3v9Lw7iHXbJ9sFpYmjZokTpzc2P941rOLipUlJjbUnrtCugLoAqtiCXWYC/do805A2x3lG16RQrZS16ISk0aZH6II7udvyKi6RzkvbNVzGDj5jK0s4ZEuG61GPKMd8Q2wuQbOpKSjPOTcXu8+KmJBeGtC5JZSF6DmTPhSHf/WR7sX4qfuERGV5nV664ugldjwou7gyQPzE7g5NmqpADGWuucQkQZSRwC3Ew1qY0FJ7jcmh6EmSgTAsy9vSTvv6cyPFGePYnTJe+kqVX2xykDK0Nrs0Rcd05uKiaqYkDKl8NfVP4TGXnens4qrnahEwtCu9OizW1UEg+47gkSLa6nZNswak0F3qi4NpE+ToFVca0KF/Agly/XLJw+YmGxYF7KPLJp+5WoJRe5/7Qor+1DkQrBJWYecyWYEWia+8Ypr3kHgwdV0gYwcuBlN/OPBPlwJTUHre8mQjgFC1msgQlTjodPowddITlt3zQnZYWFa661ptuovfbChzaXBWGlNd1wRHWVWi+518Nbn/rjkj0ful0XVV35diaJc/TYOtt/tkPMs81r7R4KtC8lm4RuSj5HcjXtgfkJCpoWD3FVytN1ezYQO/99udAaXi9GA3COiXbhrSL6IlWwMtlG4IUjjCUQpcfJpEJg7u1bg19pUiNoXMrLyxaDiL/h5FN8jW+0vGsiJXRXjyD0bPBQ+DYODltWccVWmI2+Bz7orRRAvN2H7Xv8xtU/DHzBZKkJODTmjuGZnVfz1RAGe423LHa6LzPbVd7cUepkwefza2hJB7H0ovTQiPooUOrYTnN3drhytLsJtmkNzvNi6uxaOF7FYWJq7KgWoPUD4BLqdwndye1RwiMHk+soqNZ1jKgLlB5EQ+yrcGMSX0jqXr1YNMITiSEN0YNHG3vk2C01mo1zdhk0bhjBno9q6YmIXw8ynG1UhzX+oPqwQtAHFtRr4kzTF22Jt9n3FPGb2Z3NrWKMXgQb4QN7XIiM3JSxndIpHSHW5ldSSDUqc5um+XdpvyuLUs5oqyWuztpNtXq6D9IHMyJ6VKAcPJCYu8b6SFrYe2eryBbm3bMODBgF3ILPwPtzSyA3TN2fcDBgkU6/I8esS2iC707rZAyoP+a3iw0zfqLvtrgIxo6sHPeAmg0OEQocJB7sPr4M6mRxJEvna4E1BG8kFZed5DiPDLPW8B+/9Jo4nJrKx7RQx7akLJGR4GAIliPLY4GXr1xsEhlJP7/5MU9f7TXhgon6IbU05i6vj/QgYKxybsn2jZ9Yc91N00twrjg+gCa6mOI4TFoYx0oWeXInk8kzSOgxv6I+Sc+vLuB7D3LVtDouqPKeSNi7w01EakQE5YKSBDLgKpD0ZKECf/8WUGiQzn4q50p07YrWAYol+XNO/lnqrvOl//obQ/zZ2jIYTt6PtwDFhpJU6u7ISWc2rNQ4et8x2GV/lENE4nDiBVgeQ3aN8P87gQ4nbhZJ0XpwB5Hj5vPWlRlnHIMy0Tqxe9a9vEwYUm0JH936H4QimNMZCx9lMw0VVFIHKtTqoyryH8waTRFlhgbtSzL97HwUP+nXtoZjh8A5/84pEeRCp5suCaC1i7+4b3TWmBVunfa7K5uUCgeiEY7s/GZRujKU9aRfRwSm8VrxFD6+Q0lOKhgVffNVBKcCUspjX2pjNmkSgUjvKRo8zbS/6uc0o8yb5vNwkyNzs0gCWsSl3OtrHQTiBT4oTzi3PREu/KAUDwpQrWr3I8c3Ljrw25s/tZR7SBsco0LDyKgnmattesxsVk3jzA3gnIqqV/Rbba1xuFCkfVjOmVtg8U1G2JY1y8ss066tIc9sh7pJ7umse8v8NdL9jxVOIhTYL037o8YR4bNWEJaH4wFCNqnMh3t9qOnCvvVUgRrNLQgk6ZzUegmP3dwIhBP9VVpwpxa7Y63yGu+p+FxGZho0ufuhIrBRnJOv3wBDCthKFEVElhtx6sYBndXmMm6RppinLKAj4FhMykeeL7GMrTmj54DwyuXt0DAfFSNdtRZy+JQwtDFAH7jdf5Ht+h1u63NNYQ/fWgZdckdOsvAprTqLnm3tk2DXmavpckyWr07ZJ2PSKX+h9BbFvlENd4P1ircUSIy2DGg7dyvrXOcGDBm3aJV6SRozyWqp7ctCXlW3xVbyhNyLnyFNOnxhrMee5O1BYtpd3sDBaBvGIVo92ADrKhZ5rNhB0vWqJFqpCNT5Dd3R/hu6MJHa9fx1LKsKcerqyQv1jh7j1LyYEqt7EzTKjJ6V4bz6lBExegug8zXUobjQ+XTLB56Szbm0UMym3FJZOhquifUTGL9F+jejfRV5f8cjCBG+CDjIId2Skt/iIX1ANBDqfczIpV+SPwGBgJx1magtIwGjF4nMMGpAjobH8s9+jXC4F6MKtW0txHrnK/9y5xyB48ZNyoQ6KjzXu1iVWrW6guOcGfafk66RFb3phDbwAs09H4rQxjSdtQpXzlyCrCAcnT46Ndp/4NeUep/KZFcaHPdxZuQlJUMgwtQBDUbjzoBjn8huNBkEVWvXO5tPPjh/gWgJi+LkTRn0l+XC9OsAeb9QNMqg6c+8I3hXvVskwAhvfY35yJxUr9vGQHHoHCc+UP8lK+maAePvcsbNmnxjrRScLmHA/RS5AwehDa0VSb6dCNdw2z2xuefpVWzGgiWR4IK3i3FF7A7z/YB7vl0spUHxeFJT0+0v3UEoRyxHJyCee04dQ7yT7fHp+aDaZHBCFwCm0TcWUxBNnefjg9y6tyzep0ispQwyU/qwVwqWWTJmfy7RJqe0advZsU6IqXwGIitMQBCCjL3Ny8IphDck98pM/ooLIjjM4yGu/qrkSbzAQfXf3GQZP16rH9DZ2kbat6xfw/wnrr+bhZb27xNyNZUojKH3ZMOAwvuj2SAry8NymAyuimQJ9Xm14xq0/f/ki0rfK2gyD6AOfVLN5i/DvBCGxbVH1AcYoKY5BwHVkcFO3iAL4kdW+sxhREyTmRMngrT+7FWGUCKVTqyrLCsBPs0ntMziaSMKcSyXBf7Y7Zn6+28vkUPTFp8Y9dg9/NKx2hNyYTetgqR79jp+5v288L42G/wvCMZjgXtowNFeTZai6qE7irhrfObOYvufWPl0dEJvuTXBCFZbv8Qh3IYyzO854m8kynXFTTsRAKrOzs1T/ISbcUztdT6wytBPj5V2t+MXJ0eQmwJRGvxIKScXoD2d8YCYZ1SyLhOusE1Cb7LRv9bHz74VXwL9RnaK9SOMHk+mstmK/T4mu6o2Xmxv3sr9YI5s3L2BCQORGKcVJ5hqSeroC7GaHEl+qP8UCR5cTK+Z+bT5EvODgfKYg4P8dGfOJKsViDsO2YyHFiu7zvKwuVnX1cr5g7jz4reW7Ze81JlZkWxCuJZbNCowSIt/X0UMWs8kcfaSPNRS3EjXu+ggE5T9d8I8spFVj2oKjKceaxRO4wD0j9jjzNGWAMj36QnaV8goIvJtqTok/I1q0TUqanzx+7Zk/3RLCtqFFitQHWBiMwsstyXW2tKVXDVwA4oFDcm6z2FJUu8j1i6wkzqqveoT6gmUrsgZkR3q93rH8DMPB20ZQHXcp2kTEsf63aue+zXhYG+692g5awxS2vHQBZ2rMApIp8mkyA3ZkOR4ljzqd0szH/bw/wAOXWG7blHFuCvMretaIdPx0/4ZkScN2V5CUvd1a6BaiR5EK4sf8TDQmGUygHOIjssHnmRqNoCGMCiT3EjNJ/JPeO1EX1/Cu6eQM8mK2jPZkqV4K8D+IR/2EGUgI3HhfqacVG4pw7t8rtGCGomPEnfy/4B8caKxZf4th5bS4flWvhj/Y87+U415PDGiG5cBwfanaj7C6b1ahvonJpNW1QDnYv/SNsg9Mj3usHPBm9vgiKSxOlEjNsluPaMNcU1e0mLvdGRUL9p0eylOzxuqld4ALmW+vDCjNUOIcN6S+7Z1GIcXsrBk1cl7JLqBMMBlxDJuDRAHXeVuz3EZbMQEZyTBaxhpIEylLxhA3f7CNeiRXc4YIiQl3Bvz0v1pRQ9cAeFD0kEC54V1klr7dbqXxEA9G+tbHDBXvHaRAdQVRyjwLvkxpnZyvXh0D+mGEERsnsuEAxJscNW4uD7xvZE5fgIwbP0jY5maCkzANavPWK2GC67me+Z91TYBqpr3tJ8z5eZ+sPMybI+X/wbFIXW2hrq1uEBn+y1z8jdkjHRAGZ0b0uyjtH4FrKhYCiXzLn+5AGZABntR+H0mTj6EpXPmYUdak1Uj4Y1CqcamZQ4uR2r3atBL41xS0rGJWjX09Q2zKJtdd0ypcj4ALFLkXJHc1RydRogro8cuv1t/0obMT2x5ek8EJbbqff2whrHQL8WL2vqZeF74JijS7YrMg180O4RD9JddEo/VZijwdKTei5mIBC9YnNwyOl4KHt5I1rbaL79gc0gUWRArJABr47CHO4sYh61VTRryD4IxFVsYKl8hDONmPev3Lg1mTJmh34HDmAnOOcjFf4XuLi4CqwzFGrwfIOpg3eMWNhv5RMyKjAO8USd/7SGhzUbQ/lVauZoKoTeb/tMGH8bJ4QLe9lS1Q79JIyKzI9hMSavzb39GAKMbnHNbN9Vr6pPb5rjnUTAacfWSJ1LRfD5YbsDNOlatKfaLLZpWueB7O9GXBx+7NLHPdmq2uoWkKJhQBH/hk+HDvLqzGUtzYaZeXlegtzZ3icBH1FrSKho5Hv0XbEKlSqodVBDMhjfgQeF06Q4QY4VL5Lr7kLiJxauT5kL9rd8jOWjiAVaA/jAA3ZYTLYjKbl6uJvQKoj1wk9AlvUVdDRibpZE5vTFESZfzFbvytKcNX/MS6Owp5LRaBrvqNm3S+8tPqkyBTNyCuxhppdcJmw+HXiDFWcJeRTonSvMw31Yq0rTZIFqxNIjVCH2TBQj85OUPTPV7NlymcndgS1qwTC7eXWwFD8WrBpnA4UpX+itT/9ZSjavehXGp3j+rbSMkuQ6Ev1YCneVzCHXtBLkhEF7ye4Jg7Wp0z7Zn1ZLgyxPL3Zx0jn10g3rrKL3BaHkong7+Z4CDAdPq2sCtY61SLXS1Vjr9vjrVja4Q1txknXqm+CWyducw9+LNIsRKXi1h8AitSyZTl0YS4CwgAX0Yet905gU9sIVoY11pc9KU3eJeJtIm1cchWtfUDlTo+9OGSJn8eKl9I7zB5PPh0x0i4XDcB+oz5vZTprAiN3iAI1i0eQxxYf39V9uIlsW/y3grcnOK4bZJG/YSLSFQquR+30kNUx1scM//TdvqzRfAAOSPIJjFx+y0wh8YAtHHEpG8bSsOz78c1UzfKsP5AAIAjkPby9o87JT4h0NpY7vv7no30t80V9VKSRQD2KBgNEWK0JRcXF5lN1IFOjI+jUEm/buWriTKgsVRdze/t3t9oEOfx2/WsRtIWZJBwQUbOrGGGyY+UQWcRGx8qjce21zp9ed/i01FZ2WZhC1RcsKQT8p1irVORdMaaloWogV0DomV02R6Ao88YHgKqOekSiKLt5TR+IMcctlvFS8TEbcIjEOO/1Dzhh2jjYzCaJ8pCS8z8smebHGXuSujpIjGOY1fyT0oWacNm9brD1MVg4eInU/I1m4aDLX7vrrccgH7rqwabQl/GgtFZZsLlBcgSdIOjN2BBWSNGgqtc9p4hLJd3GI/IfdgmnC3qWTvVX+gtsSN1fqn59yXjJD4Ii+1q+TdWWfx99pzwqaw+db/rmUV8zxu5OtxJ7VWZzgrZjEWzYaLtxxB2bWbB7/FlWlAxqpsDMhWjzEkwxqR3E6huoOB/HQFhmivKU+oB2yOIM0DbLaU90h1tkurQje0aw/EkopYpCSMxg9VazrusuccHseGW/gp4oQ3wDM5Y/zQxfRc7xetbrSzF65dzJgcFfR4s2aq9CvmZVlkNWQpoe99FN+HTGLK7+t49V8NvGyMTd8KszbG4eXlaHznpDj3Ls5SC79akFzucs/ZmRSHGVaYLsx9eZ1BwnkauA9pc9X5TpwulU+IIa8gAImImpGP9K/1SjKSfOqbQBzGcS5c3YMGa63g6N2PpQx8/ylP4pyh6uG9lpw2MVhqudGyn4S8UsRsunHjoaqoQol19MgBtLWW9rvU+Hy1chrXkeYCOrXpte0pL/ODgYD9CQ7tqjxzFicZFZekoYRultTnQ3shxLxSeKxFKddORKMj8xmlRtLg68PUobGzzlM6kExl79VgNCEZggAdzhfbdZuuFoBiLvl+nITFqV30RgAXa9tcvwY952pv/25Fh8GSXgmBNKv+U6zhjmPMJirzhdQDGRrqXW5WjUsoiMrugiBrsfN5gYazRcyqdo+2U4G/YIXl5gWCdn8pqQn8db9aoyMw29gQ0xUIeFNj25XHZ5Hs8vvvEcojvAaOZLPxT1ixGlOfbWv7MiH/YlBdo7c666LFuvA3YGBMV5rp3+Cz5ZDv09ynIpGfn8foXuN0k5id+dnu2dPLYq+7JdxFmIaLTiIOXJQcaBVR2RXHnqnkvq0Sl4+N3imc+GGWZkG7urtYtDCgtVJJ1kyGoecvVRzNADV8JKygSKDRY+M9+edk6uq8zgy6p7cVqvdLAm3FJRaVK1TJyLpWOJCwjKTLRcdT2L/HgNC8iFkgzWR4BRehBeRWVhqm8zDm56q2lSuOjfKd6m+jZViG2cLMZL0b2Ik0g1I/JlJvnIeob74tE2+ZwKamKPYyTKRW2aTHuRmJCdK1ESCUAKkMbRLa+uGjM4TfIRDJwC3c/cSiLnojTWj5LWw==\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" candidates","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":";","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates;"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates;"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" replaces","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" hard","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"-coded","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" values","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" tokens","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" consolid","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolid"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolid"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"ates","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" components","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" aligns","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" UI","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"db2RfIwt/EgN7Qw7W7gyu50pUYtgaOPhhUk8C6UJ85BEFwjrBoRLCVYHwFKlrlvbd5LLU3crSpUtkMwezu8tC7OELMqMgl4f77cvqql9yCqIwP6kC/ULuZhqeeBcgqwGa+QRh1QuTT5atClVUk3/pBNRXF+tCIKqQtKRNXb/R2HRcHn+MkmmITmLw/csoSsA0bBdsbUuf6F9FtUM8cbVWOQMbo6lL6nevnqWOUmKXqQeuwWCaNH+mbmgu2gGruIZAiO7lS1NmcMx1HblJj5rwhvxfbAnWqUL8bc3tPYQBT2Jwtaz6GTkGzXioY1yK19rjyXZQcXI3+gomGjZvCSemmSXjekS4II3Cp1ZFw+Ub2QTfk5ih94R5nWUxIdC5lbXyVQGkEVlhh4mfulIkwuSvKAAhzfuFeP0z9GM8ghItDrRj0QCERiukneezpU6gh3R25N4pD6aySB3LKRdMMWTIermlT96vRnAGl7zREFzeoAvxZ/Cukt1zj1hClPbz0ZIBuz0vHzF+Uaonbr6PpO9ZNSvUmDl8VBiqDC010js6ywQuRUABc8KmUVGuKXiPoirKTNlk8gdl5mis5hkPfAIBlKYgLcdMa1Mpypi9VAeVgsGxGCKCmdrxL1FwTVwz+mefenlsta1hSpybw5u8yRkdUFyOI2Aeahim5oXNDeIJjxL3xV7CGxvlq/2/ZNte7KZAfSWg8CtjJumFx+G3EaQlBLL9Py+ereiwEy+9cWZX5AhsaPrICf+gCxaODregS8wPK7CUpvtjG6hcCN57fmrVSzLEkDJm6jCADghImUrf8U6khZeILQub4NGUjUa7/KWkNUkAVIZ0ibZUMs4TVMD5un9AFNcqqnP8zoP1Li8RebzbRtTgXoa/BQogHCDAf7jFBzz/YFxphIEtWhxnwlJEfntUkvU/JYqwS+5mEg8TpRtsWZ+IaHsLtaLSwwZ71K+rAwOKGSoDcbPvUzR119j3vaVhljpM0GTLfPt07kJqHEN7Sp2Q+8PgG8MecrdTVAYYNFKYQAYuU10WZ9KhzzOKGu9Ziyd7pexl3ZDsDFnm9jS6YYB/m2tT+JybP3MTPNv2GTNwcf0A0hpc0iduC9z2JNeytRqxmLQlX2myRHaEYt8Z+Uen8GJPhZ6dosxCa2f64dv7SEYJrW2+d+AfYX2mckOP3PbQnLqJaEnKUMtkgDLLrkz3keWc2MsgjgvEE47zpWliDu1nshlBOlH6fVd0pMbyd6Ncv/h7NBAzPd0uJ6HIWBhX5vyxSBvGP4nM86xoYyfsADF68NEYhGBCE5pCDmc3rH4QrVVeWYkUHXNGIdYDsxkvt4i8jt/D3Y2cK5KMmTRtnmdU2zp0N5LUTKOHPvuFMPbaEGA8lSEEol+F24RkFFVywB8k1k40pCrmKGHN748U4iJEHXKLDBgFBRngMd2HBfK9W9Q9sPAAV8kd3o0h5pHqdevHFdA9a+UDzeALwwtz08NRx5Rfv9ZP6Nym6xJdQ6qmz64meaXSKtyydUBJYrWrCVmd9KV5MdACbLL7Y2zGGYUvgunLQkU5Ixgd2JupAt/TSfXrp2fkCaeIwlT+4Yx8LSDuNx/q6/CiUbniZgWbZOkRVhVFYS929EI5kYif1/cz4YKwN9kLTsDJQFryJQl6OEuqEfNscxeQtR93hVIqEThhuORd/tsJwXoCOh21rZyB+fi1MVqJ0G/MOSpuB0smIOk8DY5o9gurCO8KGDPNjp+lT+zpz12j20dzCtrCsdohBDL48dFgAzPxkYxI3b9RejL/ilrQ7pdPYOLstUrIlYK2q5bxOadd2SsT1935UKDUlIWr5zV63T88N5qEdsJ0EdtM/Ke/1SwvKduyasy+fbNNa17cMtRIF4t58XgriqKT1Te1ljUOb1Lq/yHGL0zYn418Kyrob5sAgTMrFGgr9NaNj7ESlhhQqJntiwBoGCgGZI432Q7Za4vbzyoFodKTK6CgC2gZWofK2zFb1QWSc5vXdQx5Xk0xvt5eQun5mWOQ7wEgljHYIVfo+NSQhnS2NiR/U3ae9Cwmi/NdG3gJpvK3fLydaKAzED/MF+nvG4xcCDxXRE7suzSObhiKrfYJhbahW8WWkXLHf9/+m0Be+nVA+kgOzq63UBVleN1c5Et02zecEnc8jA6vAsshyD6+O3WVbOxxqAo4l8P5udkH1EIFfPr1iXgEWHif686shj1MiHBcqiq5+vhn08A6VHbZ3P2B1O9T46jwvWvSQabZ7YvQ030gMmahCcgdkWLkYtn1ZPBwqpxs0J02lBG1An1kj+kUyOxu3+41yFc4UhTJ+EuYqY973iROZDN56R7hWefrxX0G/0WpkHpQUtstq0+YPNee3stS+8y9pgHqsZNdfCCJYib1IPrq0i5LsK114bWmLBQEZ1tVPHE8vZzratOTfyJg9TX3CJi7Afk2YHoHMjCzaCyOYnH3ym2vaRdzOwC2S2I06upCM+HQAqmaRMxqF/BLTeUsL+HkT52Bxq3UnZGQ1SWUDdya6IA0u2p5TOu5BFs4/A4sc7AG/Q1BWEIXbmskVWlgk6XJpHU9T+jO3PxPsTWvQGRtS8O2X7dBMCbyY99bUog+C5ljzSbPIBuIBqZKZJy/98dw4Lq5ZCBisGQi7ho4YX9svuKaCOs+Rmh2wwYYq6Fd+xH63B8DY3VsJr7Vw0qn+qUyvIzUU/S80dplg02jdY4rNM3TqFUzy1NPK2jYIy8ZIiddmZR/TlQW7KYszpa2PMPVxFrUbW3wiqCh1ffhM6JfRr22yW0L05uj871nZX2zUEkccgDC4KXN9AQ0V/WKP1of1ulo92ldHbUk1jhkN3to852uH9JkpTHniMeR/UVnaZjpalvIcexXENYYhuUVV4/gpMMAH+CYQvINqnTMcONVNR6Hrlob+ojMBK6poeGo/r5q6MnDnOaVfeFPKspRT2wA5h//oDIr3kWg+kfv9/nv+9ExN0AF5ohBI4xRrUEoQ1r/GoSn5qLYXCb+Mta3YKsmWPsMDaQrKRGz2YPA+XmByUiZW3wgpz+o7lJa4aWqenTpabgHFpzlMwlZi9/sCGPiAEKelI87jBDgMB6bSFOAhatWKHdRQecd2MNwS41st8l7WT9GhOK5LNBvqxLBeUd+5PYFIS7wYXa/j//5PFdt8/OjBx/30b4aI5u1GC1y6RKwac4HUXLuiAPW3bsD10/PHKFQ1Vo+wMNyA5yy1P6geHZSxphHGlGgXOQKhXou8rczI5PFetekzJBrVPRIZwM0kEMDz0xsbNDwmAIlwMXvyszkukS7qdJ8dRX36hWonMGbJ4xJ9sqCzeQoznytO/TkmUa0MRtCM+Zul7X+Y0i6In3qsntz39648SSjowzS+0gaXzV1ZJVwoboSefVVNp/rODgByzJTTv4+MtV6rJUrvolFH9ORQPhckIDUNFE9wiWMSENYEs67vTmFBcXIwdVRaPf2A68IhG4IPNqZb5FbkGlKdzkltOSJ/dSZOYhOeEA68dCNLmsAvu7xzOAR8reMJM5zUStVWGBuMzDkjYBm4hnANXZ6BXfHDXNMC+YZ+dFvRZXcRwra1IEcIP2DanKa/NVGn7R8iy3Qv7qHJhPZtdU+rq95KEGkZBLWfrZKwVlMtZ37yM3XF40OxGi8moNJ30dovwNdDLSVtI0ekW5i1yGXZD9bXutYm8TZJBoXo57zdVcZiRQFaMYVF2jV+HWJEzx+FB/tUwhZp0jBSiAk2pr7HGbNmV3sxPh+VIVP+Nxj3ztqbHXY0/wVcWChp3OYWuGfKhaqymkFTk3ULWCyugix1BiGRxp4+V0HVAgI14X45u4F3HSGeSzAkf+F/vuQn1NDDU4F6/qc3fgpsEUYjDXvftZIttL6XSmugIgWXmo6RSIuhXs5pOisBDJ3fc1jNs87o/6TVrDY3wlrMiIjyIt7fec3b17NtutdIJVSsjd0/mfrbMzzvnMyKK6Q34lxoLWwKIejYikfPzwSX96jDfx5Uz8WTq9nBV4TUQpb6V6Dd1xnmDnDT5qwjIpZLHwivnaVg7v/2EisLe4lx0622aTAVRFjvcI8AqqWPiSVImj80UIkcOEdJGkVvjcoC4Qb/lzMZ84SCBBeCilYy8KpXJsK9Cu1tsb3dGBR2GZ93T0Alomumjth8xMZJF8CRBsQVPEuZVwzQjw3dvleELz2cT2PtQGLA1+t+QiElEBRYrW7Bp+jOuNu0blzJbiFpj3ODT7UrXIal92iWflN0N5AakzouOuQaYoFEtJTP3Lc2HDLfWJkPI4NrVMFxD3OV08Mbo3jPNMumy3VmjcNOGtfWElY7/KrlHN6yfB43NBaYMqbArIOdnmkANMgkCxoV2xbCwz2xC9ksKx+EtKXHmBp4gs2vxlAzBIrsOV5wjVEIIwavrTvlBmnr7vENLak7usnBLPSIXYrkLOYlpY+7J1Qr0Uc2N6E+mtEtqyb/nDAyyWluOHvOPN4ZfsPByhye8RD3MjoZhPsfRYY8mvZFpDR+cBXwkA3MDDTe1ouSfG4xYvg1b0RTYZ2UEIZtBWXSWGysSRQLGbBOU6fb9nl5WkVKjPCfbrckOd7nW05FHKCCeS3cUh8ILs4osr594ti/yb2gOm5wFVeFltpvu/Ojp7Fa6jAPSDIehV2aRdSI92YQEaagosCrVRDg9GCznZ4c7W1n93oASyIHOyD8n3XF2oAXk84FOVbQvklpKPt2ASRmlY/p8Gb41rxC/ZEDLxpx3cZHOUzr1Es7c2jG2OKTFFauAebhflGbZOSBryUDamjS5Twu/NvF077a0vJ5pyZBorBWYS5Rb1AjXuCE0JCa4TQHy542tlll35tB1eW9FC6Px4Qernh0Kkq+hJ3m3uWe867VaBcFfsWAaUFjtF2QEpFuZIswPveQIeYCcVL4CCZZqctMoeOXAgdM5KLuGV9Xp0r/aVt/T5bFwBMIjPVFtSr1kT7nvip44jV0/SmqmgWtVupfPqyNJqA7EVqpjegNWcJ/R2zCg7/npXSsc4cGQU47cVz9sq8qQAQmZwRSg4Ow0vLfQNzQwNpjEYu1F0ZwQFloW9O56dvdg5EWHsJL0FrvD2C7rgeX609NWSNSD94VJ5jxZrIw4IFmjARpp5oaCrxv0bHCygTtm1YemZij6KqyAKW6o1he1TGACJB0OMFVRFoDdxaYFKDIYCazQJY66nf/MhrBuO4HsgceaSqlO5Q/P1XbcUbzx4wc+RoE+S6qw3DgzBiUBzoMwgxjuT/k3lyOTShqvdwi9AgXa/IS5sfOC9LXQf3N5bvhNOE+O4Qd4M25YKz2gXm9h4jnlMwZkVFujrnQO4f36AMyY3aXe7/ulaY54RD65G7TiXwsMH0YQ2wj+gaGgEzzLjXCkyQt90ZuCqC8E9c+XqXsXikAHYWDM4Ga4YkcXsKkPfre2IEHMpoI3thgHI3PBIxxCQ/UmoMJids/K9+jdfCfzdNatyv/5xpdHqX49b506xEmFMSROas89a8/96r1KZv7sbvSZmmnrfDcmbpwmzZV89O2eLXAtdYgqp0jHlaYVUat80gm7Av9CNdHg+v/ondgsdTiFWXig9mSUGtGenPMs4aG892EC72CvbCyHW8ID07iQHWruRr5iB+l93qw2YHi3i1qIhKIfHIyh2WpHHkFIFpU6lZ8lz7xAuTK0y00bsjq7nJ+iT3hvcIQRo0PpLuF+SeUnrU/d5mGQRm2bXvg3qlumacoRn5LWow0PE8gWFOM0hLJGBE4nPgLL7KFtTKq5HSRy1FFLGtANFNBDy17Ct4lUrLi9CFIxDJufuPDef28FgZjG5Q0wEqejLYiHdTvoBbvxBB5/P6xnpTnidLiyQMRlLRbYUM+/zDnO+gMb9/2ysEdfRbJ8l1Np9Hh8uyxd7OWOqnUpTW1vbP1gybmbvApE/jiHJcvUC+EdwGxc+abBxXInQMWg5z1C5CoDSi3qRONpBSX/Cwloi4xeOtEjENpVcbEqJ+5txEsT5k41x7X+IOXgxN2S4p827xvWJWWmEdE5K1J/LMg6VdAUcoM4omw+jhsefzbbXseAoEU8goxTMMz0r3VXiEvnYQr9uAgGqDozv1Jdow+MAy6+9XKm/fqLEfHEjpLLXkpNJAuaGim5B2pEIFtQ7mVlnZcdgPbkzj1/1sicqpajorUMPUuP6i81rHY/+HSoTfYAleuZNearLXyAJ7x1c6Yhh0vD+TF4PEw+cxlnFRwi6QuxA4LPv4EbuQYM5eN7WeGGulUvQNiO6YLvYHigKpPQ7FiVH0AHbZXR7jzOPgsSnuDsCJJ9wy+dF7DPdg6g6Id6H93qXWTXyzIhGIp1NoMKXoDixBmdwa7iU+etDqVQtkwyE9e/8MIN5uBIvFcZ70SpV7QWDteqz7Wgs7aAv2om62WPKctFPo0FWizUz6kjddbGD7KRzicxjAc0G/2H1tfbwIBvtUXNmYb6Lz9Gg1fYjCrgiBlkDYKNFlysjn4jrtuMlXnbDg1Rs2b65p8AbeDSqDBGsMjd/UXm3KfjKZJaXtX9uzz7CPRC+W662mMgC6AUGPKZL/dtwTUhR1xAkzkSapUNKupE/f9IcPKWLMQ7ZHOlJxvzXvTF8Jjqt9ecB7cDuO4h4vs8glGg9QUk6MIx5SRMPKn6FKEa9zgDpq+2+iUla1pn/MQcHcfR9tYB2rQes2ahuLN3vrS8cRaYVG49D8DLBdn7b0LJCrbMYhMJtIEUrJOVVhXUjRCzjvZuWj7eh3tvfHL/ixL4BA+rDLG2w0cu98bvXnulZCEgou1iQPBx6YR+bLmS6gYzcENwjaskixqqMHzPh/QljCOhcT3riJubfd1QVunyi2705MC7Q4M4g6Lt8SxCCrkj2iuxD/jtsy3t/PWlyB+sVTmOjEUp1Z4buqx8NlhjRgNXM37oGhCFEglPbsqs6vXX87zm60oj4CSNRX2xrBoDzVagwUJwKHaqjr6/N4CS5BNK61dty/u1sabimbO7am/+yuKR+r5TSO/l4MyVll9YEhuGlcMSvSV37sJZ2pgIyQviWpcD/wFRKqQeENJIyS8MHL8V24Z8L/QEAJSjFTk1IgxKKC2IexSl3BBE4d8Cl+sHU86uNHfXy3pffN9IHflt4uEMC2yRJzQT1Shc5ofm7ir5+3UOtq+ipeHIl+p16MS7tvX4/P4hOsexDLvUzDaC6/B4g7MXv7BJn+i2Y8FfqcJpQ//pKMqDqECDb9SCwT9o/qfOm9TCdnsZwEPnNRXZiL8JCB/KJy5DgnLr+pxtPeFH0N5BoknXATlAVcgOVQMA+AifpkvmLeylv5bgmwxw8Ji7thYCHujBC/UOMpvySeGl6lyg4IY2gMkHtUmuA6P2rhoNR8d5gL32li8h5eUI3Q/7cJkoprP+B4wIc+ncIQTKDB9WOXhGV988b9+YOe5ZeZChn1yKdvm8JtZ+8t2CkLID6Fl7GWAxbG8K+8mwTq1x/tYdZGflgiUXzq5JWQ1+Dca6YBAInEHTJsO1SJ9B0dbHJZRXhHhXySggDmoDmP5cTwH5lfqTbdFyrv+PpJBpACEXZ+gCJU1VMPip83AkTmQWe5ltiCM8heMUOEZaZci/XIGXH79J4b1Y+H/qSG9+mt9mt53av5TSSVeY/GnYy/eMcQHCr6dnDts6488QIzcn2baeMZslKURz43k+8wHGgCBlDw0v8UOusS5aSpSHf7al9boGAouKiVzaLwKvMes1i4+fyKpEAHJ0bM/zqYKqKzPZE1AHoFcbwNPbtbj+UZ6t3eEMDpe/7KMI8WkzoP6v3HsLLBFcepVwY5WRcXaChpD14gn9TGsEZ0bmEflje30As8DpaPXBKSaIJRvDefuBYObm3LyE0xZIHLISqUeGb9w8q+U+B9vp2u7a/f4pVnHiDh62BgwFlbwJBjWsU0cZTTQv5z1ehuJKquZpkwf96VGm0u7+qLHr/UiRNweADpINFgvZEcLRqsfbS5P5HY8MWHkQlOplizpopgg2de4atKtfXC6q3gsFVWJv4wnokxa2urlgleUuxNE/kOjZhTBlIwE+PGQBwMz85kRDLVYZCCAi3PoO/0WEb4Wx3fH+Asune6YrMmGz4Hhk3wV1HTzE2+1j6xiDIszjmD0Uq95TVAqQWD6sBz+Sww4CJ2F8N5dhodvrPsfpmoEZdl/lDMtuXFXl2q1XdJfv1C3PltE80IVwbu9opvocY9C95dXRSMGPl96PrL0+eRpcxy0mlCBLD+fujSNbS2ME8uMId+2I494t4eProErDzvKMKRXQia4SxTp7uJcMCOlHkGEYDw7/osgX7l46ht0zQaw2TRKRknxz1Tl2dod8M4gNAWCePnuSLTt77AZfq9vyyzqY7+jDOmy9gVCazeaUAeLi7lUYW2F1SZ9Ekws59PralQhDPz/ZmFIPT2GXarZRQIIFIqFY4NWutN16IMm+eoCetMCZDxXf7HC+/AWGOzuYn85gilx/W2I3svyYF8mxOkOjADcnQIYImyI+l1umtILvaJdMf6lfRqLG1xT/Ditp6Hd8A4Zpzm1JJ7QDwS4qz9IRBJWP2qMPhw8OhMYTE3vPN3DtJ2VccndwRTQ0D8xNdgFDb+ST6vuzhGo4G08O+DOxF/mOW8f20u4MMtNDjZbmYRL1PY1Oi3tjEeRTKnm99Fz70/ldCHsbQfVGruhxpXQXWzMDVXFpKE/j4gpN65RRHonb7aOV15jvt81TWhcUZuST26YCKWpDpGZY/rXdxk7DXK0EE40fHYb8vxb8ib8khVuDQLdjii8D00nXjjwcmrPNUp89jeB3uQfsz6tyOzNJjR07ZBiNJcuZYsNDzUsyul5sYnUBohlR0xeWvkxA16uRB3bj40DPYQBWY8SdjRFbUZn4gByzWqLJabNGXLHiv/WWYibnG0r9kBw8ZbdfD+zhNB7Dk7Ytp2pb3LCI4M4ZF5/0+Eu1FGDGMm8q8qD4VDoaCeugHYlPTO7r7I+ZHd/of3Qwe+39ocG8peQBND5E0UQqJIbojYXHpxYBvyIBHpZ7pIhDKLumlUexBydHw5cZGMggaOAJXTeHPbZl9IMYWwejagHl717Il4D+OBSppoPgTDCw3ttXjX5Ixmr5ZKWwY7KDFQwEpdOlf1GIYSZP5LR4xbOoZuICvw9aNB2yd9iJuTLtZ7giQ30OZ8lcR6bN3MAEHr40CcKPngfpNNR9UineTzyawdTX8lQg/6EaPKA/YQncLUbZuN8366FkDD7CtGjYRBHZTTmawE9oI+SJRfrKeoIL6qunGO0V1a8rUZsdVEPYNmoRpQzAM84F5s8/MkxNvd1yPvjCuygxRZIUg3HMS3iEAASAcDs+q4fr5/7ecR5F2SiUAmLQmdDKfCJsAdv1Q7s0ymiXspSJxC1rlPDKdPHawr+/NCZ8lKa+zbPzNxdjY+svEFox4Edxd+XzU6RHIcm8Y9ITkD2Fb3+DDKI5H93cb+tppxdZuSTkRcEi4rPUjw2/zwi2QHxXaBQ3ScBQQOyDUAu+hctx4nFy33eF162RRP93R1PxBJ9Ba27Z7OewA51G+1UBjBeIWasPUvdDVkQbxHH9O6lUWA4n/8sXcDi99Dkr8D53B28ticmuAb8KihOCxg0hvH1TKkX7PUHhVAZ4ZY39dOIORb5X1EMRXJ4MtGkgCPQBmlEM4dl7XLK1AZJ+9m3L55y4kC0haT8CZEiITm6OxCCnua3wsIDwUPRgvR2picyepn3iZML0+VTNiolR9Q4EdCtcjRiySniFvWuqkf0ZOv9gZ3Jyat7fuGb95Fj3GZ++t7w+Bl8IcUKx9ycGlUtGut2lm29qf4go0WBptHzqG3c/y2KOOfQg/+iRgjzFmb/60uUIfNrlkUTGePN6JmiUIr4nCsB6PEnRtni7CHfVjmAQmhZTtP0qQ2q0aC8JmMNhAF7J4G3F2yPCBN+fVtluopRuWm6EZbvnxHV/m2/H5jOmAgzd6ZbvreaFLi7z4EqRGdHrZH7xyvE0Bw83a6/92cN/SNCoZhGKM0sdwzBBTift80WqlRxJqXewnvnpGI/cwZev9w5tQ1UNBnZlL7o2HjRanpWtDAWDxUjnlpgq5Cc+UoSWDKVI8alSZ3zSiL2QKljRthZM12dOcEuzxdRSNHOzPK3I2KPp8poXNQRLTRVJCsUiDCBXsrwepBihbg3i3WnC5SAeq8fQXa+zyh9QruGoNowAr0FDHHjN/ij+eAaDzk//w5D8tlmovKdUo/5AfFzgtYJr4iHvV/wmt6Q66Dv9mVgKoXqqaeklVUuZrKCAuJuArQztwCqAALBBV2WRthr0Qzfzn43goyV8VAJ5ege5bAjHRBLNsdOEf9HqKz2sfSUM4cy8nqSn2VcBJRr7KTghleg6LSlst1mYec3tOlm7kHt6S8oQzyQOzZA+wUG35mOKbHCbB8oXngLxFg+Et3X8VHvOzmCHynwJyiOIUWO62lyYmT1WS6m9FNZYrL9if1Mf4rC3P1fbWF5KzkIVyA6Krr+NpLDnLHSAbxMjNsRLUOcOVk6qV66pmxZe5fSXHRJnbiP/RGWqL5qcBUgIOJE7uN62rcSL+M6Tk7j8Wuq+1NtjOHbbZoUkr831oWbipTMfTw8ztkcEPthuh+sfilGtGSgza0yQIYI+C1Xj6IK8OOhhSnPz6/1A3X8BGj5jobPTE/ZW7yp+a7g+/tMQZrNwnpeaq+G8hylc+Y0VVkJbSafIzjk9oORTkg6wYclBN9oGodfge5+C5EUhQpPPUeIcQj9OV+kCPi68grbETRGdU908HVkUlBF3wGWQb0UAQBe/C0n7NmF9zqVugsTnmmRH+0NLXS4tNV9AynFWP5/9KCfSDLSFS/YhU+wXDZyKKdOEVB+QH4QyxuCMP3JnApMpROv3M2XOV71D2vC9b92MH6VUYbSoMoswOZ+w7A1YJs2Zgx/pUzzq9kkfGFAxQWHEwQRYp7HfF5nktHL7GGwqbbzWK2/IyfeTM2bDDV5kqukbVTOCE82D+dA/kALGpgfdKURFBhAmd9dLaxjyDZLyTvZZ4AmEn9ZhKVxwOPLsIekm7BCdwyn7QNFsZVlyEiYTSfaFh5QjbEBXhniHVefs0gmn8smPk0zmsRx8VWJEn+o3ktPdLxqWUSfE3ZTBVgBosCmbBR1lqIXxSwEcGzHGLig+raEVzmIJ8bdK4VHAkYt9P5JyW/ubK829wQAdyVFJXj+///YOrXxdWFBoHiA8IJ3cS8aVf+PnbpC76TtjoxXAFds5eJP7nPZLDrs9lJ/O6TOlb+LplOfyDoPXXQZo0Nxscs2hDvxSKtvKleRHPA8DPxC72d6JmdQLI26kbf6C7bCaO7P5uWreJr6QwMfk86/ImT7kjAYIeE3mbPHz2dVcKfgZKpBbzPVWVoAtz7bg0IQuyXWiDgELPl5mOkkLKtM5HwhvKbgr/lu1ScGCTZe658PbSRyNcE7aDKhfaA1PcrcPdekbs0mfVVAyOLXn9Xaes0OnoJyFX6rCj5Nxqg9mJtRbkbbrfFbHym5xnHFNg8ekda4a21IJF7zEc1yjwAgXcmC/7wixHpe83DRepRzOOanlt+lf/UKPcBoiww4j1JittaxJdLoskbEj+mJfoUOxl66zvabLvngSbhyfcURJgn37rs0zhsr0miBubD9ko8Pq6EL5mKc8a35674nZLEyODL5K4X3lkcyPFCrO9q4toUo5dtmMVZfqAOAl2O7kfVCfG2z4I4eNrO9SBCGC2O/wCTfbO4CMaB58fx2SuRlWdS0+QOqgHBy97Nn8FKgRkNuttOZs7I+UBrKoB5cXfwDLUdDqMhvvt9vZZkbgGbZHEcdz/f60aZZErMFjfNDr5/Jw99MneDqKj0KN1EWnTThvTCx36RSxnyOPePP11qLABoxgJ5kbDNyHf+uS5cidwg2KkPNV+clSqTzdDTEb2ICjZz/Lb3saQ/6ucfoWjh+OdYV3dlXxZFZZnXHNJoMgAaL//wpINX76A+7QLipp5T5BLh36uh/SfIAmA3gR7pQVvKi7anZ5KE17DAzX95MxVlxBeupWr732iMhqm41p50ZNkRotrAjV+EScrrGDSm7vb/bXmtAmx/Pi5nhHiKmRdpl1K3vxju6rtWK11LBY1oUo46X3ToTC+iTzkRPqX18Tk3emC0opgpgpOrzWmLtX4sXCi6F9O9MjYv3nbPQ/XK7sMAuJQzwCgGudeJVVlk3DtAr8l02BT9Nx/IHKpqxrwad711+aLHfKm4/kl/GBywSkIkuW6Dnc0HdNcw5CuxHjHe7c/ihwkykDleM7wZWt2WPrgjdFtIx8d98B7Hoou9r3LNq0xKqudoYbjt67jy3aXeI/E6Sufb7mO1iYLIrz3xtEYJXELxein8iAtQq7qqhb9IlSQXapJ0SAt0xu0wJHBhSRLRFs1//2Wzo/ICcFHFIL/E/E6t6dWp23FuotN1BAdNdaE1pL+AaQ4ccShmaGedUYDm0t2MPQTZlE4MZvxBQSCecOV+czMw/3uFoqfW0nKkLK0MXLFFSTrqpRHgmupYIfh5aMo17Qlgy0zk3AcZ9gjNnPBAm9YqSDx+PS5L8oH8uY8q6WJNUw4vwwAVzzrDxTibmlYtXFg6qH5x11dEvJBIH55258NcVzrn+fcb2ECqkIQiy5Xb/62SShKPYosCle7YBuUhXanPE51KTNZaoh4+2oLIoS86KVpdRAeUp1iUmgmpiB3+vZAF71qKnmlaUkgGlhzmGJmpDyN3hhxOtWYeKMRxu977XwlT+mEvKJVfbODUm3pkJlPqd4zOYdRghjFCh+FxGtaK1+jMgIvxCQLTlbkrhoCuvVPh9gtInouBYG/oAc1WgrE+ZdV3PV0JkdOc/sVf7w1GTLVeqA5euWs4+v40nK5JMwwruZfNOMc4JWc1jBT7GeOeOgdRAu6R/KEhst7ByLu908YiZbcolT3XNDPraeKyeTWGZQ1DxWGXdPKWmmvl5NwlMbT8KcZIoJRjtyrpg15yUsXk1sGnTTItAm4JMleZF9oZ/vBPYbKTeG3Rt6zm61EEtoNZje+tTzMFXlxhn64CAYb7VYBfiu7MRewgJBkItjwpaG1WJqvwF7Bjiqd1oqBkQdR0427klgSyCz3v9Lw7iHXbJ9sFpYmjZokTpzc2P941rOLipUlJjbUnrtCugLoAqtiCXWYC/do805A2x3lG16RQrZS16ISk0aZH6II7udvyKi6RzkvbNVzGDj5jK0s4ZEuG61GPKMd8Q2wuQbOpKSjPOTcXu8+KmJBeGtC5JZSF6DmTPhSHf/WR7sX4qfuERGV5nV664ugldjwou7gyQPzE7g5NmqpADGWuucQkQZSRwC3Ew1qY0FJ7jcmh6EmSgTAsy9vSTvv6cyPFGePYnTJe+kqVX2xykDK0Nrs0Rcd05uKiaqYkDKl8NfVP4TGXnens4qrnahEwtCu9OizW1UEg+47gkSLa6nZNswak0F3qi4NpE+ToFVca0KF/Agly/XLJw+YmGxYF7KPLJp+5WoJRe5/7Qor+1DkQrBJWYecyWYEWia+8Ypr3kHgwdV0gYwcuBlN/OPBPlwJTUHre8mQjgFC1msgQlTjodPowddITlt3zQnZYWFa661ptuovfbChzaXBWGlNd1wRHWVWi+518Nbn/rjkj0ful0XVV35diaJc/TYOtt/tkPMs81r7R4KtC8lm4RuSj5HcjXtgfkJCpoWD3FVytN1ezYQO/99udAaXi9GA3COiXbhrSL6IlWwMtlG4IUjjCUQpcfJpEJg7u1bg19pUiNoXMrLyxaDiL/h5FN8jW+0vGsiJXRXjyD0bPBQ+DYODltWccVWmI2+Bz7orRRAvN2H7Xv8xtU/DHzBZKkJODTmjuGZnVfz1RAGe423LHa6LzPbVd7cUepkwefza2hJB7H0ovTQiPooUOrYTnN3drhytLsJtmkNzvNi6uxaOF7FYWJq7KgWoPUD4BLqdwndye1RwiMHk+soqNZ1jKgLlB5EQ+yrcGMSX0jqXr1YNMITiSEN0YNHG3vk2C01mo1zdhk0bhjBno9q6YmIXw8ynG1UhzX+oPqwQtAHFtRr4kzTF22Jt9n3FPGb2Z3NrWKMXgQb4QN7XIiM3JSxndIpHSHW5ldSSDUqc5um+XdpvyuLUs5oqyWuztpNtXq6D9IHMyJ6VKAcPJCYu8b6SFrYe2eryBbm3bMODBgF3ILPwPtzSyA3TN2fcDBgkU6/I8esS2iC707rZAyoP+a3iw0zfqLvtrgIxo6sHPeAmg0OEQocJB7sPr4M6mRxJEvna4E1BG8kFZed5DiPDLPW8B+/9Jo4nJrKx7RQx7akLJGR4GAIliPLY4GXr1xsEhlJP7/5MU9f7TXhgon6IbU05i6vj/QgYKxybsn2jZ9Yc91N00twrjg+gCa6mOI4TFoYx0oWeXInk8kzSOgxv6I+Sc+vLuB7D3LVtDouqPKeSNi7w01EakQE5YKSBDLgKpD0ZKECf/8WUGiQzn4q50p07YrWAYol+XNO/lnqrvOl//obQ/zZ2jIYTt6PtwDFhpJU6u7ISWc2rNQ4et8x2GV/lENE4nDiBVgeQ3aN8P87gQ4nbhZJ0XpwB5Hj5vPWlRlnHIMy0Tqxe9a9vEwYUm0JH936H4QimNMZCx9lMw0VVFIHKtTqoyryH8waTRFlhgbtSzL97HwUP+nXtoZjh8A5/84pEeRCp5suCaC1i7+4b3TWmBVunfa7K5uUCgeiEY7s/GZRujKU9aRfRwSm8VrxFD6+Q0lOKhgVffNVBKcCUspjX2pjNmkSgUjvKRo8zbS/6uc0o8yb5vNwkyNzs0gCWsSl3OtrHQTiBT4oTzi3PREu/KAUDwpQrWr3I8c3Ljrw25s/tZR7SBsco0LDyKgnmattesxsVk3jzA3gnIqqV/Rbba1xuFCkfVjOmVtg8U1G2JY1y8ss066tIc9sh7pJ7umse8v8NdL9jxVOIhTYL037o8YR4bNWEJaH4wFCNqnMh3t9qOnCvvVUgRrNLQgk6ZzUegmP3dwIhBP9VVpwpxa7Y63yGu+p+FxGZho0ufuhIrBRnJOv3wBDCthKFEVElhtx6sYBndXmMm6RppinLKAj4FhMykeeL7GMrTmj54DwyuXt0DAfFSNdtRZy+JQwtDFAH7jdf5Ht+h1u63NNYQ/fWgZdckdOsvAprTqLnm3tk2DXmavpckyWr07ZJ2PSKX+h9BbFvlENd4P1ircUSIy2DGg7dyvrXOcGDBm3aJV6SRozyWqp7ctCXlW3xVbyhNyLnyFNOnxhrMee5O1BYtpd3sDBaBvGIVo92ADrKhZ5rNhB0vWqJFqpCNT5Dd3R/hu6MJHa9fx1LKsKcerqyQv1jh7j1LyYEqt7EzTKjJ6V4bz6lBExegug8zXUobjQ+XTLB56Szbm0UMym3FJZOhquifUTGL9F+jejfRV5f8cjCBG+CDjIId2Skt/iIX1ANBDqfczIpV+SPwGBgJx1magtIwGjF4nMMGpAjobH8s9+jXC4F6MKtW0txHrnK/9y5xyB48ZNyoQ6KjzXu1iVWrW6guOcGfafk66RFb3phDbwAs09H4rQxjSdtQpXzlyCrCAcnT46Ndp/4NeUep/KZFcaHPdxZuQlJUMgwtQBDUbjzoBjn8huNBkEVWvXO5tPPjh/gWgJi+LkTRn0l+XC9OsAeb9QNMqg6c+8I3hXvVskwAhvfY35yJxUr9vGQHHoHCc+UP8lK+maAePvcsbNmnxjrRScLmHA/RS5AwehDa0VSb6dCNdw2z2xuefpVWzGgiWR4IK3i3FF7A7z/YB7vl0spUHxeFJT0+0v3UEoRyxHJyCee04dQ7yT7fHp+aDaZHBCFwCm0TcWUxBNnefjg9y6tyzep0ispQwyU/qwVwqWWTJmfy7RJqe0advZsU6IqXwGIitMQBCCjL3Ny8IphDck98pM/ooLIjjM4yGu/qrkSbzAQfXf3GQZP16rH9DZ2kbat6xfw/wnrr+bhZb27xNyNZUojKH3ZMOAwvuj2SAry8NymAyuimQJ9Xm14xq0/f/ki0rfK2gyD6AOfVLN5i/DvBCGxbVH1AcYoKY5BwHVkcFO3iAL4kdW+sxhREyTmRMngrT+7FWGUCKVTqyrLCsBPs0ntMziaSMKcSyXBf7Y7Zn6+28vkUPTFp8Y9dg9/NKx2hNyYTetgqR79jp+5v288L42G/wvCMZjgXtowNFeTZai6qE7irhrfObOYvufWPl0dEJvuTXBCFZbv8Qh3IYyzO854m8kynXFTTsRAKrOzs1T/ISbcUztdT6wytBPj5V2t+MXJ0eQmwJRGvxIKScXoD2d8YCYZ1SyLhOusE1Cb7LRv9bHz74VXwL9RnaK9SOMHk+mstmK/T4mu6o2Xmxv3sr9YI5s3L2BCQORGKcVJ5hqSeroC7GaHEl+qP8UCR5cTK+Z+bT5EvODgfKYg4P8dGfOJKsViDsO2YyHFiu7zvKwuVnX1cr5g7jz4reW7Ze81JlZkWxCuJZbNCowSIt/X0UMWs8kcfaSPNRS3EjXu+ggE5T9d8I8spFVj2oKjKceaxRO4wD0j9jjzNGWAMj36QnaV8goIvJtqTok/I1q0TUqanzx+7Zk/3RLCtqFFitQHWBiMwsstyXW2tKVXDVwA4oFDcm6z2FJUu8j1i6wkzqqveoT6gmUrsgZkR3q93rH8DMPB20ZQHXcp2kTEsf63aue+zXhYG+692g5awxS2vHQBZ2rMApIp8mkyA3ZkOR4ljzqd0szH/bw/wAOXWG7blHFuCvMretaIdPx0/4ZkScN2V5CUvd1a6BaiR5EK4sf8TDQmGUygHOIjssHnmRqNoCGMCiT3EjNJ/JPeO1EX1/Cu6eQM8mK2jPZkqV4K8D+IR/2EGUgI3HhfqacVG4pw7t8rtGCGomPEnfy/4B8caKxZf4th5bS4flWvhj/Y87+U415PDGiG5cBwfanaj7C6b1ahvonJpNW1QDnYv/SNsg9Mj3usHPBm9vgiKSxOlEjNsluPaMNcU1e0mLvdGRUL9p0eylOzxuqld4ALmW+vDCjNUOIcN6S+7Z1GIcXsrBk1cl7JLqBMMBlxDJuDRAHXeVuz3EZbMQEZyTBaxhpIEylLxhA3f7CNeiRXc4YIiQl3Bvz0v1pRQ9cAeFD0kEC54V1klr7dbqXxEA9G+tbHDBXvHaRAdQVRyjwLvkxpnZyvXh0D+mGEERsnsuEAxJscNW4uD7xvZE5fgIwbP0jY5maCkzANavPWK2GC67me+Z91TYBqpr3tJ8z5eZ+sPMybI+X/wbFIXW2hrq1uEBn+y1z8jdkjHRAGZ0b0uyjtH4FrKhYCiXzLn+5AGZABntR+H0mTj6EpXPmYUdak1Uj4Y1CqcamZQ4uR2r3atBL41xS0rGJWjX09Q2zKJtdd0ypcj4ALFLkXJHc1RydRogro8cuv1t/0obMT2x5ek8EJbbqff2whrHQL8WL2vqZeF74JijS7YrMg180O4RD9JddEo/VZijwdKTei5mIBC9YnNwyOl4KHt5I1rbaL79gc0gUWRArJABr47CHO4sYh61VTRryD4IxFVsYKl8hDONmPev3Lg1mTJmh34HDmAnOOcjFf4XuLi4CqwzFGrwfIOpg3eMWNhv5RMyKjAO8USd/7SGhzUbQ/lVauZoKoTeb/tMGH8bJ4QLe9lS1Q79JIyKzI9hMSavzb39GAKMbnHNbN9Vr6pPb5rjnUTAacfWSJ1LRfD5YbsDNOlatKfaLLZpWueB7O9GXBx+7NLHPdmq2uoWkKJhQBH/hk+HDvLqzGUtzYaZeXlegtzZ3icBH1FrSKho5Hv0XbEKlSqodVBDMhjfgQeF06Q4QY4VL5Lr7kLiJxauT5kL9rd8jOWjiAVaA/jAA3ZYTLYjKbl6uJvQKoj1wk9AlvUVdDRibpZE5vTFESZfzFbvytKcNX/MS6Owp5LRaBrvqNm3S+8tPqkyBTNyCuxhppdcJmw+HXiDFWcJeRTonSvMw31Yq0rTZIFqxNIjVCH2TBQj85OUPTPV7NlymcndgS1qwTC7eXWwFD8WrBpnA4UpX+itT/9ZSjavehXGp3j+rbSMkuQ6Ev1YCneVzCHXtBLkhEF7ye4Jg7Wp0z7Zn1ZLgyxPL3Zx0jn10g3rrKL3BaHkong7+Z4CDAdPq2sCtY61SLXS1Vjr9vjrVja4Q1txknXqm+CWyducw9+LNIsRKXi1h8AitSyZTl0YS4CwgAX0Yet905gU9sIVoY11pc9KU3eJeJtIm1cchWtfUDlTo+9OGSJn8eKl9I7zB5PPh0x0i4XDcB+oz5vZTprAiN3iAI1i0eQxxYf39V9uIlsW/y3grcnOK4bZJG/YSLSFQquR+30kNUx1scM//TdvqzRfAAOSPIJjFx+y0wh8YAtHHEpG8bSsOz78c1UzfKsP5AAIAjkPby9o87JT4h0NpY7vv7no30t80V9VKSRQD2KBgNEWK0JRcXF5lN1IFOjI+jUEm/buWriTKgsVRdze/t3t9oEOfx2/WsRtIWZJBwQUbOrGGGyY+UQWcRGx8qjce21zp9ed/i01FZ2WZhC1RcsKQT8p1irVORdMaaloWogV0DomV02R6Ao88YHgKqOekSiKLt5TR+IMcctlvFS8TEbcIjEOO/1Dzhh2jjYzCaJ8pCS8z8smebHGXuSujpIjGOY1fyT0oWacNm9brD1MVg4eInU/I1m4aDLX7vrrccgH7rqwabQl/GgtFZZsLlBcgSdIOjN2BBWSNGgqtc9p4hLJd3GI/IfdgmnC3qWTvVX+gtsSN1fqn59yXjJD4Ii+1q+TdWWfx99pzwqaw+db/rmUV8zxu5OtxJ7VWZzgrZjEWzYaLtxxB2bWbB7/FlWlAxqpsDMhWjzEkwxqR3E6huoOB/HQFhmivKU+oB2yOIM0DbLaU90h1tkurQje0aw/EkopYpCSMxg9VazrusuccHseGW/gp4oQ3wDM5Y/zQxfRc7xetbrSzF65dzJgcFfR4s2aq9CvmZVlkNWQpoe99FN+HTGLK7+t49V8NvGyMTd8KszbG4eXlaHznpDj3Ls5SC79akFzucs/ZmRSHGVaYLsx9eZ1BwnkauA9pc9X5TpwulU+IIa8gAImImpGP9K/1SjKSfOqbQBzGcS5c3YMGa63g6N2PpQx8/ylP4pyh6uG9lpw2MVhqudGyn4S8UsRsunHjoaqoQol19MgBtLWW9rvU+Hy1chrXkeYCOrXpte0pL/ODgYD9CQ7tqjxzFicZFZekoYRultTnQ3shxLxSeKxFKddORKMj8xmlRtLg68PUobGzzlM6kExl79VgNCEZggAdzhfbdZuuFoBiLvl+nITFqV30RgAXa9tcvwY952pv/25Fh8GSXgmBNKv+U6zhjmPMJirzhdQDGRrqXW5WjUsoiMrugiBrsfN5gYazRcyqdo+2U4G/YIXl5gWCdn8pqQn8db9aoyMw29gQ0xUIeFNj25XHZ5Hs8vvvEcojvAaOZLPxT1ixGlOfbWv7MiH/YlBdo7c666LFuvA3YGBMV5rp3+Cz5ZDv09ynIpGfn8foXuN0k5id+dnu2dPLYq+7JdxFmIaLTiIOXJQcaBVR2RXHnqnkvq0Sl4+N3imc+GGWZkG7urtYtDCgtVJJ1kyGoecvVRzNADV8JKygSKDRY+M9+edk6uq8zgy6p7cVqvdLAm3FJRaVK1TJyLpWOJCwjKTLRcdT2L/HgNC8iFkgzWR4BRehBeRWVhqm8zDm56q2lSuOjfKd6m+jZViG2cLMZL0b2Ik0g1I/JlJvnIeob74tE2+ZwKamKPYyTKRW2aTHuRmJCdK1ESCUAKkMbRLa+uGjM4TfIRDJwC3c/cSiLnojTWj5LWw==\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI with"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI with the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI with the"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI with the design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI with the design"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" system","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI with the design system"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI with the design system"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI with the design system."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI with the design system."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_end","contentIndex":1,"content":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI with the design system.","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI with the design system.","textSignature":"{\"v\":1,\"id\":\"E+erobMPty3kFLmPhy4iafDX6i3IHQ0XdjemsrB7TARqzNcGA90UZTh2SmajRxqdA5MpSErVQgF8AIbkQudeGkaovjv0DOs4cz9IXMJsSgQPKZxwLxE2MKoWTBdW/7lk12Gb0vAyFBMS3XqXYNaPSWAcv8yoSMdji762M8EwGQEgJScjJjEP0BwPSJy4Ur1Je1FO9ZnlDK9ljkzZgjc9SUZCAiegviVfU2InZWpEchjXj1pvJoPpc6sJzr6LolZCVDKawQrU+pBPldNaGfFy5woj0jXsaY0VeL6tilRTd5MB57RY1LQHzXmYOp8tZ7ofTTuyWZjUi2NmS0QstZWpMLSFit4RamLtsYj1ier/FnTjTQpEzHZyWsqNePxIVmuSBG1S0y3mMndg6HJfWnkkY9TjL++HkL+3\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI with the design system.","textSignature":"{\"v\":1,\"id\":\"E+erobMPty3kFLmPhy4iafDX6i3IHQ0XdjemsrB7TARqzNcGA90UZTh2SmajRxqdA5MpSErVQgF8AIbkQudeGkaovjv0DOs4cz9IXMJsSgQPKZxwLxE2MKoWTBdW/7lk12Gb0vAyFBMS3XqXYNaPSWAcv8yoSMdji762M8EwGQEgJScjJjEP0BwPSJy4Ur1Je1FO9ZnlDK9ljkzZgjc9SUZCAiegviVfU2InZWpEchjXj1pvJoPpc6sJzr6LolZCVDKawQrU+pBPldNaGfFy5woj0jXsaY0VeL6tilRTd5MB57RY1LQHzXmYOp8tZ7ofTTuyWZjUi2NmS0QstZWpMLSFit4RamLtsYj1ier/FnTjTQpEzHZyWsqNePxIVmuSBG1S0y3mMndg6HJfWnkkY9TjL++HkL+3\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"e4pgfxENamsbezKOGsqgiRBcAyJCxQpcwss4NK9eeoM3BsjdTc5rlc4vMFZK/oPXTCaJY7Htv7OgBizl2HrfRSw8hF0dQFPhv2390LKkn5Af6X+zJMIcGDimY/dC9PdiI+37hZIQbqNy+M4ax1bGAqKKDR7Gqn5HITKXpJSnTwMCwEBjrwQ73c6NCA+VyY7qRcecP2bcuPxfGSWTNnuFiFzfZ4+e84nzIitI6pQjQakW9+uiSyPmgAOYFMuCiz9MckBBoEG1e/U1yeh7jEoy/8HEVQ8bJMjkikH0OPge4h+GmnSZl8ByjuhtExtFC60nY1DZ+BVdU5EgIBHnqPgMTUJ/xu0OAsaY/3N9iaCCF+nLIBcoiyiPzDHau6JEEdoY09kN8+4p/SLp8Mrjc40hn0nxh/unWvd1cw=="}}
{"type":"message_end","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI with the design system.","textSignature":"{\"v\":1,\"id\":\"E+erobMPty3kFLmPhy4iafDX6i3IHQ0XdjemsrB7TARqzNcGA90UZTh2SmajRxqdA5MpSErVQgF8AIbkQudeGkaovjv0DOs4cz9IXMJsSgQPKZxwLxE2MKoWTBdW/7lk12Gb0vAyFBMS3XqXYNaPSWAcv8yoSMdji762M8EwGQEgJScjJjEP0BwPSJy4Ur1Je1FO9ZnlDK9ljkzZgjc9SUZCAiegviVfU2InZWpEchjXj1pvJoPpc6sJzr6LolZCVDKawQrU+pBPldNaGfFy5woj0jXsaY0VeL6tilRTd5MB57RY1LQHzXmYOp8tZ7ofTTuyWZjUi2NmS0QstZWpMLSFit4RamLtsYj1ier/FnTjTQpEzHZyWsqNePxIVmuSBG1S0y3mMndg6HJfWnkkY9TjL++HkL+3\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":10392,"output":2136,"cacheRead":2560,"cacheWrite":0,"totalTokens":15088,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"+O1dr0TuGM4DjxUtjTzxQbsiWBsXFhu38qigPh3gpvbstabxZNuSj+rgrehRVZzN0RzEAGXxKR6UKDpRriG2Dfd85PIpQTA7z56S6E0MsenuOuoJi9GN+1f9PaFHJdREXpLJ7/7Kb3Qwe/frRseDyqxYTBW15l7PQj8LTN1JACl53hCnoCwTkM1WKmGrMjB+SCdwQCKFAgaCosLCTLtGBGhe6mroFg5jl0J+yXOLKZyMRQ6xBrIUwPgfEOWz/GjBk9c81ieZXX8B/3mi235SX9j+VSCLHhKgf9jSioCIF8HwMW64+9CwlTdRVozPoLd9t8dnJgSQBP0ZRIldrF6Nuzg5k03CsCyeXSTESgdG4QwZdFmkoWPWesBxCKcEBBUlGfwNaV6Q/BR9xLXcgwwKA3HAVaaAF74LPg=="}}
{"type":"turn_end","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI with the design system.","textSignature":"{\"v\":1,\"id\":\"E+erobMPty3kFLmPhy4iafDX6i3IHQ0XdjemsrB7TARqzNcGA90UZTh2SmajRxqdA5MpSErVQgF8AIbkQudeGkaovjv0DOs4cz9IXMJsSgQPKZxwLxE2MKoWTBdW/7lk12Gb0vAyFBMS3XqXYNaPSWAcv8yoSMdji762M8EwGQEgJScjJjEP0BwPSJy4Ur1Je1FO9ZnlDK9ljkzZgjc9SUZCAiegviVfU2InZWpEchjXj1pvJoPpc6sJzr6LolZCVDKawQrU+pBPldNaGfFy5woj0jXsaY0VeL6tilRTd5MB57RY1LQHzXmYOp8tZ7ofTTuyWZjUi2NmS0QstZWpMLSFit4RamLtsYj1ier/FnTjTQpEzHZyWsqNePxIVmuSBG1S0y3mMndg6HJfWnkkY9TjL++HkL+3\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":10392,"output":2136,"cacheRead":2560,"cacheWrite":0,"totalTokens":15088,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"+O1dr0TuGM4DjxUtjTzxQbsiWBsXFhu38qigPh3gpvbstabxZNuSj+rgrehRVZzN0RzEAGXxKR6UKDpRriG2Dfd85PIpQTA7z56S6E0MsenuOuoJi9GN+1f9PaFHJdREXpLJ7/7Kb3Qwe/frRseDyqxYTBW15l7PQj8LTN1JACl53hCnoCwTkM1WKmGrMjB+SCdwQCKFAgaCosLCTLtGBGhe6mroFg5jl0J+yXOLKZyMRQ6xBrIUwPgfEOWz/GjBk9c81ieZXX8B/3mi235SX9j+VSCLHhKgf9jSioCIF8HwMW64+9CwlTdRVozPoLd9t8dnJgSQBP0ZRIldrF6Nuzg5k03CsCyeXSTESgdG4QwZdFmkoWPWesBxCKcEBBUlGfwNaV6Q/BR9xLXcgwwKA3HAVaaAF74LPg=="},"toolResults":[]}
{"type":"agent_end","messages":[{"role":"user","content":[{"type":"text","text":"IMPORTANT: This is a real scenario. Choose which existing frontend skill or skills to load from this directory. Inspect the skill folders if needed. User request: \"Our cards repeat the same pattern everywhere and the tokens drifted from the design system. Extract reusable pieces and bring things back into alignment.\" Return exactly three lines: Skills: ...\\nOrder: ...\\nWhy: ..."}],"timestamp":1775940969867},{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"jgYdn95cVVHnlK15jThXKFDi33TIiftGgJ3KsFCBxHXEsahrFLdD0FasGJUgNCkeOcxeIY69Uov0UFzm14w6bXzDtcojyk6Ag5VQhaFL5qlN7jIx+PoYas6+44wNoADbeprx/QgMXqpVM/npNx2Q2/TAy6+olAHJpjQzDDRUpoXUDTl4td0FQnENvJHPwBfopfdChLXxFLVrgba2DHQFS0bkhAcTwVRDdB6oKVta0Y137lB78cQNbLREd/NCSIdbsRIKJb4NMH80a3ZOt4W3ptSWrKjKBMLP0oz9jf+y/PYV1dpXNoJZDv+wz2dBRpBtGvurkUuGr8UWn6PVGJffthMHo4b+OhAPgCb777bzFlwoHUccvES4fKDQxVSrxKNU7ekg3FBZgfU95Bl/vv9xj9+R7aynkFZLVIGke09zJTut0mXYdolBaX7Hjkt2zcad2lPoSdgRz9QYEzlOM/8T5Mi1ynB0k4FTepTHagkWs+fbmvbNPnjIozJFxkuLt1BvCNnJOu6d+5NOIYJZoHLRUHlXJHaqrsaBDMfTWUX5XG0V3BT37SzT+XzPdcWYpf49+FOyedA70P3fluswT70Xo934zdQuRTa5tI+vgqZe9RroF2RTwoNKPsO6qwve9B+q9gzFoVERsQVyuqHBrZxkdSKRNSg0Lik/pNMltRuadVt5UrMG8SFv3ykj7VCv7qdGR3BBnHkQhLgWjnhVko1inlU2cWwYteoQef+2ZSi02dQy2ZqyPHyfNeduwADFf6J/+su25Dh8eeZNyFiJWmgEBBQIMJXmqmEl21XnFst8Np+P2Oh2x1XXye+Qj4k3xUYeCCYjIhHa1XaIsCIoJyOvbK48iLwmsDYObz77Gqy6HgDCDN9ShF9qHWIfmnzULaGtQjb6+0IxfRcBPOl7dA50fX1ln5ECqDrNxgX8beY86aHLhh2kGk6Su7oZ+cWbeJQ7PHnITmdysrk7x058m1WaueFqhIOEIkhKng5Fl1VonaoMPmf8OsiKTE05IN+w2OuMzt+mp7jWZ7R/CoWgD8hQrR2L46tYd00sm02w8VNBfLQx9B9Yajvyyq70jVfB6TWEcv1h83AEPqCNdgAMB2/MG02ph3Fqa8E/91cdwc7hmDPKJZ7oqwpMj/eBgt/QBRe5CrWa2yG/pSJmLPN15XHTof4VPS9UHJ3Bj3b07d8pDFEchD2Od3U7+wNm6ukloeKYk58G2HWnPNAUxIhqx8jYrbW9YcpD81VJVuQkHjGpe10r0A22UbeKY1DJXOXEywZVyuA4tW8aW7q58CD3MbBv0xk39UkBW7EKU8a+fPm5xKKy9js0b7Y5J/JhVIED27hUmfj4aoTOdn/tUXf5N8xnc3PcJru3BlK++II61nK0Dx10shS3IvCAJXtLJQVtdnGZj7G1pfs8pspxgC5Kru5wHG3jly2CNOWBQERJRnMTqFlyj8UxdTAIn2WEC0vXj4QHpYZZqQZpzYr3REQxy8uHghw1/FbUHy63z1Z31SOdzfUQUi/DPYir8juTagYq85ZV1/xGmGL6LL38+yiOqKs93pQuWLfD3juNk+Ml9rLsUD/vMit7iEDc2c2vH7Vrcb3r6bYiSX3J7oQXEJx0qq8TdanC97SnpPToEeQWmm5qnEsotzrJTnWA/Q6DD9ixi8Z0WLRrnvW+DA1lEhVho7z2Hu9ltZOEI/qhjVR2luuxVEZU4mOKEaF8EUKfdt8nK7puRx1/yM8fdso11/GFyrcwkj//REJ1+Avpct2bo8QPzu9z6hWHlQqp+1YEX/iHUaT/8ppYAkYIE0YYpefomD2j2SF4aetB7UAV17yMEKWt+NzGEEV5zrNMyGSWh/J3N0Bqd4LMPuQmgVA1MMBB07O0qr1/jKIBSXOLzE99DXEgyGVXALAXz4ED8WDBsZCG/P6hoPStrdcs1CJwMCs1hV676ZJxsY6H4jDS7reQPOZufr/MNN6RkZKgSf58B+d8EwEN8IZfIWgfT8wASEkSgAnYH2xAN5I8KDNGhPOwyQ/IZQTZklJHPjg45mJYk0JFABekgF7FPllVjQH8yPXiUhJEtxvxXGbamoR8JTxIj5efM7rmuwSJYf2P85hhcUGtSMAjOCkpKD48K6KXzcLGoOshw1hYozdg01fHInkMOzBJ+J85vlxF2n/wRDsxJZXta/3WkyrA7Wafj396idKmomX0FnaloEoapgNbCvBvbjtqGhxs50LfAQRNTuO1d9ZBx4KSklpwcJf/Y6xcKUeHgIDczuny2Hpq1svj6xyDhC+1MEKN6bs9kuko59T4Y1NBlrlC1h+qZt8IrHPl4L08rd+q/gurWCtbQ0WNvWQU8RWwXpf4rsax5+A9+mN3P1nje0TQybrWBGG46LvIrJW9xZBUrkAoJONdFliwXftVmLzjsziJfK3flEe8eXPRIwDZBiWmTLs/YJSFShiI7ReM/VS5FgQc1Y8x9OBFbyOeCAYFu8ynGabJnIbDKdmEjOaC5TFE+k1jkYgo7ZAHUt6zJtgwHaSDbmJAvqqqHE+8Kx7zAMvdxsUGNQdIFWUs74kdciX/Iz/gLHvM9LUVTkkXs6lrSF85rAtipkOiD7ZDcf4lkBS57PBXV/QmqbnjEq5O5QC6P+X+UDw7tsuc4ESwcYMOYIEUQCNEBnyQyWsz3pLP5UpfSwW3GS9vQtIqArXG9Nd9UJu+Yn4L/NuIBW58q+BTlZJ8yiUEvIv+oPqB3XWtyhZCrTFnpdptoe7yld/GL1+1YBgr2BLW4rJO8M5hfjL/f+0ZU1JbX+PR0dt0ojbLAOD7u0J/Grs/BPUiU4r18WuKJxbv+gF9wR5/TKWSxusJmDz2KE6TsFlDco389Buu3ThWSH/7rp6wbW2Tks6GJvSoL4a5aNIgw0Eryk0hcDxjtrbk0V+EtPifTJeZTRAd+A/xP55DOSirrivPOC4rE9sERVkhiEt5oX1wP7FKxiCUFd8blVgQDb2LJU99wJ7LuK7P85slNP3bg6vdvc5E+DGBlHc7Q3WrRKGbkbXsFYO8Exf+bd5FRvTPF/AKrkxrJYdnHmaXE7PeIdo/Y4hLbUvUMtqdMo1qQmsWWyeKPF2McNaW+Gatwoqdqrjm4jSV2kSnfBWYE/pqUsrSUFUTOd/lAQtqKMQRsiLgJEvuig7sEfGIelEopfS5l5rXJYd1GkUf3JQVi/CtRqtmTQ7iTyeUs1aNvcS79pSxXXwAkl7j2uFX/TdE+SLlWVRT16dHJ3z5L1Qkyi9hPFALahObY2bPfUsPHbgO40yn96YqW4GpVoSMdh34YTc8guvHSHcrob+JidHjMranUF4lks+aieHF8Cn6UfnDbHs/cHEkXi7lDfYV0xRaU4rfAjq5ULPe53J17pRQv5K7JKjKW3dRXpzA4wPY7Iyi4T28PgRD/BV3mRC5ct+3p+y7vd7f/kMFtfjZ/EFnqX0ENkr94fO6ra0LPfYIRduS8t1aE3+qmX1gG61rOPu+BE7h94Uhx5HNTOqkN610bOF85Yf+Ne2UIXyg+zopKy4044Jox6XT6bcGeLKqvcYWKYScVPZQThk9wHrhzq0KouOAZRtDdozvXY0mVEbKE2eH58/tmFfcuEUurGwYjkx0odhIQxKWP0x4D1PAQ4Wb3NEWw3JZBJU1UTgLvR7VRGJ+V0C0FnOTEGh8QnAEsJlSLOQUCxvU012njngqzkRk6GE2lc1qaktmRLF69nWt3VlPjjPQaDiod1ekIxfhb69+fK9gFK98g6Xb1OxU1TClx4JbEGj7YS/OqlisKeXlBuxaqpaOTao6IjtTiGQ7NLwHiOX3zrzx27DyLgO9\",\"id\":\"L917bwsa3nlWxE+J1IXeXH3SEr4NrfkwrgwgtvF1evHogFQBKdI++NFj72NNMLIUrKY96B/f57GSSdV4vVfa1Ijn2Yvo5NTEdkWt6x/LUm7GMn8ibjuzPzpk6C7x2vVtvRPRqiCBRFb/J0xOQBRSmWxo5FJnpL1j0eL8RvvbvVbQNCfcAsHuKaGFJ1o4zBGYmLrX+nKiM2I0fD3Ki6L/LmA97AXSDDWL9LgGlkgPfQ/rJ0WhrYThthakWDoN5flBRBaiaraXABwn2npxF111WD70xrYxiV552SP2uskveeb2sQtVmg5Uqt8Y0pE/4CK4ZZ8+JMODczdl3rLEItVmE9RQ1bOAQm/b/HDUXdwYNhignoujxEodYS/FRRY6ga8KeRGyGEO4FCxH44riKyOWHZI4iQ==\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","name":"ls","arguments":{"path":"/home/alex/.pi/agent/skills/frontend"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":2667,"output":306,"cacheRead":0,"cacheWrite":0,"totalTokens":2973,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775940971044,"responseId":"FdvoSkW5eBfJcybVMtKWwCY29YCEuVzxB39GBrUzMMCRlFUY23DucrJzYKnJRlrO3P+JrAQSwcS3AzfQK/9Zd4gsaFVC61Tnz3EO88NEvJ/+H/nwWEzPwvn5gQ3MlE4/hWqB4uJKW3BnZna0RwMqu19/fnbmWo7jMEiSXA/dKM7VLE5mAMfp3LQCaaFQ4j9z5olYzFcW5Zv6HgOTsf452oF8NLqiOxFOrxbhmuElS7J5q5UU9WZb+TzWIbVvmvaeeeou0PypRojra02LnGRwmeahT1WY9RSQbmWQ/6FroozfZ2QzxWDv2wJR79jL7CnzIyLVTJaZEOAn/mK0ykSOhQ8zDcyQ7pRm2SER93gkdxekU9I7n9fmGd7Sf4ncMgkiw4uLbfY8ksivhklCGTi4jNOCioKb"},{"role":"toolResult","toolCallId":"call_qaC8yn6T1JmMrl8sDHjea9i5|ej8DUtjwYYu9lHRQpKOgegV/h8jtzq7CL/8WhO1BEFutEacqhni58fsfpVjGv60xEOMVr06ffASHY8F+icnAzPLyOjVqjwOERQNclszz2IovfiU9KSb+qJ3Oq7qhNBIia8TrGIl5+SLc+bb5Ibtdk94dogqXH/rdWZclg8GJH+F5SCRP5lQrmAQYIqRqtbOS+PxmOojKh6gvVOx+CnIY6W4dt7WyoU7oDrgQ3Ex+oPYmT7eu9qhLY84Vtwa2BtGuPvTXMNazu/ZoLQvjCd9iJQnuP4e0RpvECb6neGvT20Ve90fgaS+ysRN1rxLSwsS8jq9QXNapz1O52yAK1qYckGSSjk+ZmXyDrxdJnzofN7lPFR6kgZym3NGasAQSZmxvrASrjMbYm/FwF7ck7tRWq5QJAQ==","toolName":"ls","content":[{"type":"text","text":".pi/\nadapt/\nanimate/\narrange/\naudit/\nbolder/\nclarify/\ncolorize/\ncritique/\ndelight/\ndistill/\nextract/\nfrontend-design/\nharden/\nnormalize/\nonboard/\noptimize/\noverdrive/\npolish/\nquieter/\nteach-impeccable/\ntypeset/"}],"isError":false,"timestamp":1775940974426},{"role":"assistant","content":[{"type":"thinking","thinking":"**Evaluating skill loading**\n\nThe instructions say to choose existing frontend skills, but I need to include \"caveman\" no matter what. So, I’ll list them all: \"Skills: caveman, extract, colorize, frontend-design.\" For the order, I’m thinking, \"Order: caveman -> extract -> colorize -> frontend-design\" makes sense, but maybe starting with \"audit\" or \"normalize\" is better? I wonder if \"audit\" or \"colorize\" should come first when figuring out token drift.\n\n**Choosing frontend skills**\n\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\n\n**Inspecting skill descriptions**\n\nI’m looking at the skills available: \"audit,\" \"extract,\" \"frontend-design,\" and \"normalize.\" I want to start with \"caveman\" first in the order. I think I should keep this concise, focusing on \"audit\" and \"extract,\" plus either \"frontend-design\" or \"normalize.\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"EhILDL1iw4VK0h2ipQMMYOaZwsZ/BkbCB9c61UCgYHUuDVdf5Twjm0UEsQlc16zgKLTpdN4GYqWsWrwVSJ71RIAek+kWcYYOuNeoVH+uju72np59MrvLjFgQ8cyoyKLxl1YSlcTSMMOE+765kJ+6lQipGPsZQjAW8cKhxXdN+owyLaNzihSyAgxvD1wNqLIsj36MIKWIQ+IC4WqLnNf/qzTDQqGv67SsjvZwb9YmdfG4CvVQCmKhi7M3RaL1deND7LwhFZiMhQma20kSv4M5cGdh+2Y1s7fa7cRYcEyNpHvULfufeWXuLzslrr3JLEX1ECcdZ1pySETZNfMyQ7Bqu35i2zsRKsXrH/Yr4gZmUlFWlfhLag74KXnfxvTQGfhMZid3UewFCRXW+K1+IdCw+1WLrg==\",\"summary\":[{\"text\":\"**Evaluating skill loading**\\n\\nThe instructions say to choose existing frontend skills, but I need to include \\\"caveman\\\" no matter what. So, I’ll list them all: \\\"Skills: caveman, extract, colorize, frontend-design.\\\" For the order, I’m thinking, \\\"Order: caveman -> extract -> colorize -> frontend-design\\\" makes sense, but maybe starting with \\\"audit\\\" or \\\"normalize\\\" is better? I wonder if \\\"audit\\\" or \\\"colorize\\\" should come first when figuring out token drift.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nI’m considering a few skills: 1) caveman (brief mode), 2) audit to identify token drift, 3) extract for reusable components, 4) frontend-design to align with the design system, and 5) normalize for applying tokens and CSS variables. I need to keep it simple and pick just a small set. I'd go for audit, extract, frontend-design, and normalize. But since I need to include caveman, I should list it with 2-3 of the frontend skills.\",\"type\":\"summary_text\"},{\"text\":\"**Inspecting skill descriptions**\\n\\nI’m looking at the skills available: \\\"audit,\\\" \\\"extract,\\\" \\\"frontend-design,\\\" and \\\"normalize.\\\" I want to start with \\\"caveman\\\" first in the order. I think I should keep this concise, focusing on \\\"audit\\\" and \\\"extract,\\\" plus either \\\"frontend-design\\\" or \\\"normalize.\\\" But which one should I pick? I need to check the skill directories to review the descriptions, so I’ll read the SKILL.md files for these candidates to make an informed choice. Time to dig into the details!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/audit/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":484,"output":962,"cacheRead":2560,"cacheWrite":0,"totalTokens":4006,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775940974429,"responseId":"ne8qzSbGOSg4QPKPAMgRtHxbYLUNtGitqhqLzZz0CG6IO5y2TWDwBM0AZnY0iUal1NNkCwx7DtdEu0Q2k4mCfrpPv1YlSm4UbrxdJHT7iXPbUiqAhB7pAUwIl6+hyb0cRxnyH8IP3ns7B5gjWChkdUqavcREgklue+pqo+vKFJkIPus8w8A+VFAKh/EydmsuVgrjG8pAmWFsX9FX0rgqwk7zniYOmGknmG3+60dpCVQxXB6HzBTdWXRbNMPpozHtEFdyWDa0Bx19yEmXDghO/cRro7ZkcgKIHfsHki7JIpFlVyyLX/2UGpVcRPQWaqeRIeEAzjtCGz3zwQBEJxK6kVxKM2TNRJKQd+ZmvZnyNUGsNUq7NQ+tRJK9ytJb0TiJwUbkmZ3W2u+qSJAiwiBowlyFcCc6"},{"role":"toolResult","toolCallId":"call_hQAGXs7CpHkdF78AsGT3kwcs|v9wDiI+k/sqgSvtu3m/wKG8ksIL5IkUg8Bqr2NB3sDjGnMrZDRSbKRyD3j80yjvRNMrk6UsF215BMuhvuN1PxXmENTgttUgUWRsoxEcDAh77HqjBWzcwYC7C3MwMIBbj7FaXBAG1FGmJahQsTqtdpLUI5bhq9VpDDg3GblOT9Lny7XNFw1gNjR8t2bergeYBZoM7iMUE+6WYGF2kXEoSh4vcuZdBWyCXIbUJsSYBfTcYWlyF0/vFUFHqg2WznuyP/NTp+IirYGfZ9rh7vgFM+qIrbIngJLcxt3pq2AjBeXaBjKQ5U71rhaFBqahQQOoOJ4crYep/R/fU6PDL/aImJPEx/cd+gtmrGFpVyRdmznQ0/yz+odedwCbr03N2ut9Gqe3zUPReCtFHnt7Uw0hStrjeyw==","toolName":"read","content":[{"type":"text","text":"---\nname: audit\ndescription: Run technical quality checks across accessibility, performance, theming, responsive design, and anti-patterns. Generates a scored report with P0-P3 severity ratings and actionable plan. Use when the user wants an accessibility check, performance audit, or technical quality review.\n---\n\n## MANDATORY PREPARATION\n\nInvoke /frontend-design — it contains design principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first.\n\n---\n\nRun systematic **technical** quality checks and generate a comprehensive report. Don't fix issues — document them for other commands to address.\n\nThis is a code-level audit, not a design critique. Check what's measurable and verifiable in the implementation.\n\n## Diagnostic Scan\n\nRun comprehensive checks across 5 dimensions. Score each dimension 0-4 using the criteria below.\n\n### 1. Accessibility (A11y)\n\n**Check for**:\n- **Contrast issues**: Text contrast ratios < 4.5:1 (or 7:1 for AAA)\n- **Missing ARIA**: Interactive elements without proper roles, labels, or states\n- **Keyboard navigation**: Missing focus indicators, illogical tab order, keyboard traps\n- **Semantic HTML**: Improper heading hierarchy, missing landmarks, divs instead of buttons\n- **Alt text**: Missing or poor image descriptions\n- **Form issues**: Inputs without labels, poor error messaging, missing required indicators\n\n**Score 0-4**: 0=Inaccessible (fails WCAG A), 1=Major gaps (few ARIA labels, no keyboard nav), 2=Partial (some a11y effort, significant gaps), 3=Good (WCAG AA mostly met, minor gaps), 4=Excellent (WCAG AA fully met, approaches AAA)\n\n### 2. Performance\n\n**Check for**:\n- **Layout thrashing**: Reading/writing layout properties in loops\n- **Expensive animations**: Animating layout properties (width, height, top, left) instead of transform/opacity\n- **Missing optimization**: Images without lazy loading, unoptimized assets, missing will-change\n- **Bundle size**: Unnecessary imports, unused dependencies\n- **Render performance**: Unnecessary re-renders, missing memoization\n\n**Score 0-4**: 0=Severe issues (layout thrash, unoptimized everything), 1=Major problems (no lazy loading, expensive animations), 2=Partial (some optimization, gaps remain), 3=Good (mostly optimized, minor improvements possible), 4=Excellent (fast, lean, well-optimized)\n\n### 3. Theming\n\n**Check for**:\n- **Hard-coded colors**: Colors not using design tokens\n- **Broken dark mode**: Missing dark mode variants, poor contrast in dark theme\n- **Inconsistent tokens**: Using wrong tokens, mixing token types\n- **Theme switching issues**: Values that don't update on theme change\n\n**Score 0-4**: 0=No theming (hard-coded everything), 1=Minimal tokens (mostly hard-coded), 2=Partial (tokens exist but inconsistently used), 3=Good (tokens used, minor hard-coded values), 4=Excellent (full token system, dark mode works perfectly)\n\n### 4. Responsive Design\n\n**Check for**:\n- **Fixed widths**: Hard-coded widths that break on mobile\n- **Touch targets**: Interactive elements < 44x44px\n- **Horizontal scroll**: Content overflow on narrow viewports\n- **Text scaling**: Layouts that break when text size increases\n- **Missing breakpoints**: No mobile/tablet variants\n\n**Score 0-4**: 0=Desktop-only (breaks on mobile), 1=Major issues (some breakpoints, many failures), 2=Partial (works on mobile, rough edges), 3=Good (responsive, minor touch target or overflow issues), 4=Excellent (fluid, all viewports, proper touch targets)\n\n### 5. Anti-Patterns (CRITICAL)\n\nCheck against ALL the **DON'T** guidelines in the frontend-design skill. Look for AI slop tells (AI color palette, gradient text, glassmorphism, hero metrics, card grids, generic fonts) and general design anti-patterns (gray on color, nested cards, bounce easing, redundant copy).\n\n**Score 0-4**: 0=AI slop gallery (5+ tells), 1=Heavy AI aesthetic (3-4 tells), 2=Some tells (1-2 noticeable), 3=Mostly clean (subtle issues only), 4=No AI tells (distinctive, intentional design)\n\n## Generate Report\n\n### Audit Health Score\n\n| # | Dimension | Score | Key Finding |\n|---|-----------|-------|-------------|\n| 1 | Accessibility | ? | [most critical a11y issue or \"--\"] |\n| 2 | Performance | ? | |\n| 3 | Responsive Design | ? | |\n| 4 | Theming | ? | |\n| 5 | Anti-Patterns | ? | |\n| **Total** | | **??/20** | **[Rating band]** |\n\n**Rating bands**: 18-20 Excellent (minor polish), 14-17 Good (address weak dimensions), 10-13 Acceptable (significant work needed), 6-9 Poor (major overhaul), 0-5 Critical (fundamental issues)\n\n### Anti-Patterns Verdict\n**Start here.** Pass/fail: Does this look AI-generated? List specific tells. Be brutally honest.\n\n### Executive Summary\n- Audit Health Score: **??/20** ([rating band])\n- Total issues found (count by severity: P0/P1/P2/P3)\n- Top 3-5 critical issues\n- Recommended next steps\n\n### Detailed Findings by Severity\n\nTag every issue with **P0-P3 severity**:\n- **P0 Blocking**: Prevents task completion — fix immediately\n- **P1 Major**: Significant difficulty or WCAG AA violation — fix before release\n- **P2 Minor**: Annoyance, workaround exists — fix in next pass\n- **P3 Polish**: Nice-to-fix, no real user impact — fix if time permits\n\nFor each issue, document:\n- **[P?] Issue name**\n- **Location**: Component, file, line\n- **Category**: Accessibility / Performance / Theming / Responsive / Anti-Pattern\n- **Impact**: How it affects users\n- **WCAG/Standard**: Which standard it violates (if applicable)\n- **Recommendation**: How to fix it\n- **Suggested command**: Which command to use (prefer: /animate, /quieter, /optimize, /adapt, /clarify, /distill, /delight, /onboard, /normalize, /audit, /harden, /polish, /extract, /bolder, /arrange, /typeset, /critique, /colorize, /overdrive)\n\n### Patterns & Systemic Issues\n\nIdentify recurring problems that indicate systemic gaps rather than one-off mistakes:\n- \"Hard-coded colors appear in 15+ components, should use design tokens\"\n- \"Touch targets consistently too small (<44px) throughout mobile experience\"\n\n### Positive Findings\n\nNote what's working well — good practices to maintain and replicate.\n\n## Recommended Actions\n\nList recommended commands in priority order (P0 first, then P1, then P2):\n\n1. **[P?] `/command-name`** — Brief description (specific context from audit findings)\n2. **[P?] `/command-name`** — Brief description (specific context)\n\n**Rules**: Only recommend commands from: /animate, /quieter, /optimize, /adapt, /clarify, /distill, /delight, /onboard, /normalize, /audit, /harden, /polish, /extract, /bolder, /arrange, /typeset, /critique, /colorize, /overdrive. Map findings to the most appropriate command. End with `/polish` as the final step if any fixes were recommended.\n\nAfter presenting the summary, tell the user:\n\n> You can ask me to run these one at a time, all at once, or in any order you prefer.\n>\n> Re-run `/audit` after fixes to see your score improve.\n\n**IMPORTANT**: Be thorough but actionable. Too many P3 issues creates noise. Focus on what actually matters.\n\n**NEVER**:\n- Report issues without explaining impact (why does this matter?)\n- Provide generic recommendations (be specific and actionable)\n- Skip positive findings (celebrate what works)\n- Forget to prioritize (everything can't be P0)\n- Report false positives without verification\n\nRemember: You're a technical quality auditor. Document systematically, prioritize ruthlessly, cite specific code locations, and provide clear paths to improvement."}],"isError":false,"timestamp":1775940985954},{"role":"assistant","content":[{"type":"toolCall","id":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/frontend-design/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":2584,"output":33,"cacheRead":2560,"cacheWrite":0,"totalTokens":5177,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775940985960,"responseId":"FNBTthE06M2/fLDOM/K894FBfAdQAVFhtf2TPOs9HNd4rR3159o2tLEuwuTiDRMfDsZ7w4YKU9RkkcwH8vgq1urXJaYIg3dir0SURvSrtn9Taoto7iVWTscFZNMW/qPP4QBIAmOpKIijbUNag6irlWV/xf2guGJ4ccOLbK/qJuI2MSszHoi0SeXVf7hJNooRuJps8kHtCuqRngxMtYGtXMwnjL5JkV18E+W71TwabHWtAl7+sNOJKNHZnLpfmgvIbKKqrEz30jI1inaBnGTAUInI1h3dor2nTll2IIeWItR9eJ+Za+F/4WeBo1qBCoEJggBqzzYxhjkdHrHkqbyULn8BtE/AA8cMCQTjQoOOPBLWz2ysz5YMMrToU8e7TC/VeVRlNTWE2BWGRMiXdM2rhRP8FMFBTQnd6A=="},{"role":"toolResult","toolCallId":"call_VkO6m3McSEfzY9Xu2BliRqqb|1dERoTY/iFTorGBG8pO3aVr37T+Sc8zkGrW0M24F/ptVXLdyrchQMcxWa67079ne12M7FFm7KBkSmRf/YP3Fa2d0Btx/n86UBFR85msAbgFV7xdZ93DfMM/F+mbvEF0HXTpTrjkZv+I/JJ6LtzRHRSQlWiUpZf7je/ZQXAlYyAx6GQKdHf7w3CvG8MQKpwr56HjacegbtaCDJ3XdmEGxiP+z0lH9EslME0u8OTlBjMGkOy++9R/dZpJ/iMT+Z5vhQCpaQ+pvSxA2EkD/vxsPuqe+brp42evgCuAg1giLSEOWKB/3mvrWgEsIw3nDPDs1ixi54iqqyM43PMazM6SEkZoBA6n3e5HZbGZ6ipHgZ8VEzn0JOAtiSQ90Q3Rbf7jRWuAciZmzRY4y+TMMK2FPGsRTYRx6CBs=","toolName":"read","content":[{"type":"text","text":"---\nname: frontend-design\ndescription: Create distinctive, production-grade frontend interfaces with high design quality. Generates creative, polished code that avoids generic AI aesthetics. Use when the user asks to build web components, pages, artifacts, posters, or applications, or when any design skill requires project context.\nlicense: Apache 2.0. Based on Anthropic's frontend-design skill. See NOTICE.md for attribution.\n---\n\nThis skill guides creation of distinctive, production-grade frontend interfaces that avoid generic \"AI slop\" aesthetics. Implement real working code with exceptional attention to aesthetic details and creative choices.\n\n## Context Gathering Protocol\n\nDesign skills produce generic output without project context. You MUST have confirmed design context before doing any design work.\n\n**Required context** — every design skill needs at minimum:\n- **Target audience**: Who uses this product and in what context?\n- **Use cases**: What jobs are they trying to get done?\n- **Brand personality/tone**: How should the interface feel?\n\nIndividual skills may require additional context — check the skill's preparation section for specifics.\n\n**CRITICAL**: You cannot infer this context by reading the codebase. Code tells you what was built, not who it's for or what it should feel like. Only the creator can provide this context.\n\n**Gathering order:**\n1. **Check current instructions (instant)**: If your loaded instructions already contain a **Design Context** section, proceed immediately.\n2. **Check .impeccable.md (fast)**: If not in instructions, read `.impeccable.md` from the project root. If it exists and contains the required context, proceed.\n3. **Run teach-impeccable (REQUIRED)**: If neither source has context, you MUST run /teach-impeccable NOW before doing anything else. Do NOT skip this step. Do NOT attempt to infer context from the codebase instead.\n\n---\n\n## Design Direction\n\nCommit to a BOLD aesthetic direction:\n- **Purpose**: What problem does this interface solve? Who uses it?\n- **Tone**: Pick an extreme: brutally minimal, maximalist chaos, retro-futuristic, organic/natural, luxury/refined, playful/toy-like, editorial/magazine, brutalist/raw, art deco/geometric, soft/pastel, industrial/utilitarian, etc. There are so many flavors to choose from. Use these for inspiration but design one that is true to the aesthetic direction.\n- **Constraints**: Technical requirements (framework, performance, accessibility).\n- **Differentiation**: What makes this UNFORGETTABLE? What's the one thing someone will remember?\n\n**CRITICAL**: Choose a clear conceptual direction and execute it with precision. Bold maximalism and refined minimalism both work—the key is intentionality, not intensity.\n\nThen implement working code that is:\n- Production-grade and functional\n- Visually striking and memorable\n- Cohesive with a clear aesthetic point-of-view\n- Meticulously refined in every detail\n\n## Frontend Aesthetics Guidelines\n\n### Typography\n→ *Consult [typography reference](reference/typography.md) for scales, pairing, and loading strategies.*\n\nChoose fonts that are beautiful, unique, and interesting. Pair a distinctive display font with a refined body font.\n\n**DO**: Use a modular type scale with fluid sizing (clamp)\n**DO**: Vary font weights and sizes to create clear visual hierarchy\n**DON'T**: Use overused fonts—Inter, Roboto, Arial, Open Sans, system defaults\n**DON'T**: Use monospace typography as lazy shorthand for \"technical/developer\" vibes\n**DON'T**: Put large icons with rounded corners above every heading—they rarely add value and make sites look templated\n\n### Color & Theme\n→ *Consult [color reference](reference/color-and-contrast.md) for OKLCH, palettes, and dark mode.*\n\nCommit to a cohesive palette. Dominant colors with sharp accents outperform timid, evenly-distributed palettes.\n\n**DO**: Use modern CSS color functions (oklch, color-mix, light-dark) for perceptually uniform, maintainable palettes\n**DO**: Tint your neutrals toward your brand hue—even a subtle hint creates subconscious cohesion\n**DON'T**: Use gray text on colored backgrounds—it looks washed out; use a shade of the background color instead\n**DON'T**: Use pure black (#000) or pure white (#fff)—always tint; pure black/white never appears in nature\n**DON'T**: Use the AI color palette: cyan-on-dark, purple-to-blue gradients, neon accents on dark backgrounds\n**DON'T**: Use gradient text for \"impact\"—especially on metrics or headings; it's decorative rather than meaningful\n**DON'T**: Default to dark mode with glowing accents—it looks \"cool\" without requiring actual design decisions\n\n### Layout & Space\n→ *Consult [spatial reference](reference/spatial-design.md) for grids, rhythm, and container queries.*\n\nCreate visual rhythm through varied spacing—not the same padding everywhere. Embrace asymmetry and unexpected compositions. Break the grid intentionally for emphasis.\n\n**DO**: Create visual rhythm through varied spacing—tight groupings, generous separations\n**DO**: Use fluid spacing with clamp() that breathes on larger screens\n**DO**: Use asymmetry and unexpected compositions; break the grid intentionally for emphasis\n**DON'T**: Wrap everything in cards—not everything needs a container\n**DON'T**: Nest cards inside cards—visual noise, flatten the hierarchy\n**DON'T**: Use identical card grids—same-sized cards with icon + heading + text, repeated endlessly\n**DON'T**: Use the hero metric layout template—big number, small label, supporting stats, gradient accent\n**DON'T**: Center everything—left-aligned text with asymmetric layouts feels more designed\n**DON'T**: Use the same spacing everywhere—without rhythm, layouts feel monotonous\n\n### Visual Details\n**DO**: Use intentional, purposeful decorative elements that reinforce brand\n**DON'T**: Use glassmorphism everywhere—blur effects, glass cards, glow borders used decoratively rather than purposefully\n**DON'T**: Use rounded elements with thick colored border on one side—a lazy accent that almost never looks intentional\n**DON'T**: Use sparklines as decoration—tiny charts that look sophisticated but convey nothing meaningful\n**DON'T**: Use rounded rectangles with generic drop shadows—safe, forgettable, could be any AI output\n**DON'T**: Use modals unless there's truly no better alternative—modals are lazy\n\n### Motion\n→ *Consult [motion reference](reference/motion-design.md) for timing, easing, and reduced motion.*\n\nFocus on high-impact moments: one well-orchestrated page load with staggered reveals creates more delight than scattered micro-interactions.\n\n**DO**: Use motion to convey state changes—entrances, exits, feedback\n**DO**: Use exponential easing (ease-out-quart/quint/expo) for natural deceleration\n**DO**: For height animations, use grid-template-rows transitions instead of animating height directly\n**DON'T**: Animate layout properties (width, height, padding, margin)—use transform and opacity only\n**DON'T**: Use bounce or elastic easing—they feel dated and tacky; real objects decelerate smoothly\n\n### Interaction\n→ *Consult [interaction reference](reference/interaction-design.md) for forms, focus, and loading patterns.*\n\nMake interactions feel fast. Use optimistic UI—update immediately, sync later.\n\n**DO**: Use progressive disclosure—start simple, reveal sophistication through interaction (basic options first, advanced behind expandable sections; hover states that reveal secondary actions)\n**DO**: Design empty states that teach the interface, not just say \"nothing here\"\n**DO**: Make every interactive surface feel intentional and responsive\n**DON'T**: Repeat the same information—redundant headers, intros that restate the heading\n**DON'T**: Make every button primary—use ghost buttons, text links, secondary styles; hierarchy matters\n\n### Responsive\n→ *Consult [responsive reference](reference/responsive-design.md) for mobile-first, fluid design, and container queries.*\n\n**DO**: Use container queries (@container) for component-level responsiveness\n**DO**: Adapt the interface for different contexts—don't just shrink it\n**DON'T**: Hide critical functionality on mobile—adapt the interface, don't amputate it\n\n### UX Writing\n→ *Consult [ux-writing reference](reference/ux-writing.md) for labels, errors, and empty states.*\n\n**DO**: Make every word earn its place\n**DON'T**: Repeat information users can already see\n\n---\n\n## The AI Slop Test\n\n**Critical quality check**: If you showed this interface to someone and said \"AI made this,\" would they believe you immediately? If yes, that's the problem.\n\nA distinctive interface should make someone ask \"how was this made?\" not \"which AI made this?\"\n\nReview the DON'T guidelines above—they are the fingerprints of AI-generated work from 2024-2025.\n\n---\n\n## Implementation Principles\n\nMatch implementation complexity to the aesthetic vision. Maximalist designs need elaborate code with extensive animations and effects. Minimalist or refined designs need restraint, precision, and careful attention to spacing, typography, and subtle details.\n\nInterpret creatively and make unexpected choices that feel genuinely designed for the context. No design should be the same. Vary between light and dark themes, different fonts, different aesthetics. NEVER converge on common choices across generations.\n\nRemember: the model is capable of extraordinary creative work. Don't hold back—show what can truly be created when thinking outside the box and committing fully to a distinctive vision."}],"isError":false,"timestamp":1775940988160},{"role":"assistant","content":[{"type":"toolCall","id":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/teach-impeccable/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":5227,"output":37,"cacheRead":2560,"cacheWrite":0,"totalTokens":7824,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775940988169,"responseId":"9F+g4Bl2XF0K8kMJuNdoncqiNJW0xBtgYGc87YSxOXGi0T3cRByFjO8vssVErIO1DgAkI9NSrBxI/Mu73C3Q7/cZA3lXWhdRzsiZrBkvbqTcrTF6ovLI9ahGDkKVhOBEhn1je5PU7/mXM4FjvLTUJh7eHzdfDCxtm1gD2+g9tLio74WeZl7EMWseM+9tm7rD2HtGBb5SiURT+HfC2oXDufWEaLGTuAQLwSSzyyJ3S8sYYtSgAhDXp7SP+intO/moly+BG3yu8v9d0sw84tLzJtSRjR8tQbvfr5sUaOMzYzYw35RA1X/m58Hj8TVVySpMjv9ZbnRylc0gHlmfemocPU4czBvspV11s+Pt3JL43GegwvUldBNLpDpMzepY3gg0Ppcm04Yz6INvKUXVGQg5do4wwjGA/9qzWw=="},{"role":"toolResult","toolCallId":"call_IVNedTv3S1YDKZMUEFbHLteH|ck0aH5JAxGkTKKf5eVOoYWGxmNCOlCriaWLGARBr016lNeRc3tku2wB2fUntzdEyecLLufMdwnoQ7ik9LSO+RSolbFpb9iKX0cflHs88sX6ptx/UJtA4GK305nfLBTyLqQ88+uSH2OjOJIJqfCsYI+q45FbkvZu5m8OTHpCjChwDiGpoWJWLvM/S3UVx1uVZf3pPJmPBbVO+VmiLCyFMUBqCPsdwST1F4X0pKRl1c2nww+Ww03/eM/QdmrU7KzYf6jGWrjm4d44oOffHjNRczHaB/Gk0cIY0cKpKcxDuDF1vvECFJoBY+bEmuGA9lDGDOdFrcf+KXDEMBeOF0Zx9AwYzH+GPRUqcRbcQvgFYb8tWS13YuhswrbAIGf5EoY8l67P5PI+mLwGASuejO5IUqUA7TYv0ZEg=","toolName":"read","content":[{"type":"text","text":"---\nname: teach-impeccable\ndescription: One-time setup that gathers design context for your project and saves it to your AI config file. Run once to establish persistent design guidelines.\n---\n\nGather design context for this project, then persist it for all future sessions.\n\n## Step 1: Explore the Codebase\n\nBefore asking questions, thoroughly scan the project to discover what you can:\n\n- **README and docs**: Project purpose, target audience, any stated goals\n- **Package.json / config files**: Tech stack, dependencies, existing design libraries\n- **Existing components**: Current design patterns, spacing, typography in use\n- **Brand assets**: Logos, favicons, color values already defined\n- **Design tokens / CSS variables**: Existing color palettes, font stacks, spacing scales\n- **Any style guides or brand documentation**\n\nNote what you've learned and what remains unclear.\n\n## Step 2: Ask UX-Focused Questions\n\nask the user directly to clarify what you cannot infer. Focus only on what you couldn't infer from the codebase:\n\n### Users & Purpose\n- Who uses this? What's their context when using it?\n- What job are they trying to get done?\n- What emotions should the interface evoke? (confidence, delight, calm, urgency, etc.)\n\n### Brand & Personality\n- How would you describe the brand personality in 3 words?\n- Any reference sites or apps that capture the right feel? What specifically about them?\n- What should this explicitly NOT look like? Any anti-references?\n\n### Aesthetic Preferences\n- Any strong preferences for visual direction? (minimal, bold, elegant, playful, technical, organic, etc.)\n- Light mode, dark mode, or both?\n- Any colors that must be used or avoided?\n\n### Accessibility & Inclusion\n- Specific accessibility requirements? (WCAG level, known user needs)\n- Considerations for reduced motion, color blindness, or other accommodations?\n\nSkip questions where the answer is already clear from the codebase exploration.\n\n## Step 3: Write Design Context\n\nSynthesize your findings and the user's answers into a `## Design Context` section:\n\n```markdown\n## Design Context\n\n### Users\n[Who they are, their context, the job to be done]\n\n### Brand Personality\n[Voice, tone, 3-word personality, emotional goals]\n\n### Aesthetic Direction\n[Visual tone, references, anti-references, theme]\n\n### Design Principles\n[3-5 principles derived from the conversation that should guide all design decisions]\n```\n\nWrite this section to `.impeccable.md` in the project root. If the file already exists, update the Design Context section in place.\n\nThen ask the user directly to clarify what you cannot infer. whether they'd also like the Design Context appended to AGENTS.md. If yes, append or update the section there as well.\n\nConfirm completion and summarize the key design principles that will now guide all future work."}],"isError":false,"timestamp":1775940989973},{"role":"assistant","content":[{"type":"thinking","thinking":"**Choosing frontend skills**\n\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\n\n**Identifying frontend skills**\n\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\n\n**Selecting frontend skills**\n\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\n\n**Defining necessary tasks**\n\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\n\n**Determining frontend skills**\n\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \"Why\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"cBX75HI27ohQY2o6UxC2Ba7IIe+vM0b3HWbdspN3va6GxkXvcGrRBKkVsmZ7Bx63WDMpndiPMnZMeuMRjeDlcU0/aLdVCOrxznCQ20OEH6VdlQ6dOWpdS8dtTegXQbeEkwfN0LcxFhXCjIRA5mVB0BvJZElKlpKekVltTpRAoDthr573h0xa4ZokftRlO/rym8W7qjpf5Vfi1D4wAPz8Vq8HydzDKvS8BjSv6WaTLOHrUBo4jgQIsan3KLwydNG7VI3MayZAGxbPq5ZhUeLqMecdcHm8LSTIuCEq2aqmBMdF2uVyOORJLmkmUasUCrfHkGg0tMdDE++HAZ2MXPV7Qoz7Bpdqr1lmTYtPTAUE/bPJY76oeRWcHLoZWUugt865PvF+tH2gpo8wwmGabNkBkROp24UMRIE=\",\"summary\":[{\"text\":\"**Choosing frontend skills**\\n\\nI need to respond to the user about which frontend skills to load from the directory. They want to extract reusable pieces and align design tokens, so I'll look at the inspected files earlier, which included audit, frontend-design, and teach-impeccable. Out of those, I’ll consider which 2-3 skills are most relevant to help with the code-level refactor and meet the user’s needs. Let’s identify the best fit!\",\"type\":\"summary_text\"},{\"text\":\"**Identifying frontend skills**\\n\\nThe user has asked me to select existing frontend skills to load from a directory, and I need to keep my response to exactly three lines. I'm considering options like extract, normalize, and colorize, or perhaps extract, audit, and frontend-design. However, I need to remember not to select too many. The Project guidelines say to always include a specific caveman skill, so I’ll need to figure out if I should include it while following the user’s request.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user has asked which frontend skills to load from the directory, and while the caveman skill is essential to include, it's not part of that specific directory. They’re looking for a selection of frontend skills since they want to extract reusable pieces and align tokens. This makes my task a bit tricky, as I need to balance the required caveman skill with the need to follow their request for just frontend skills. Let's figure this out!\",\"type\":\"summary_text\"},{\"text\":\"**Defining necessary tasks**\\n\\nI’m identifying the skills I'll need for this project. It seems like the extract skill is essential for pulling reusable components, while colorize would help align color tokens. Normalizing tokens also seems important. I should run an audit to locate any hard-coded colors, while frontend-design is necessary for context, even if my main focus is on code-level refactoring. Following instructions means I may need to gather design context with the teach-impeccable skill as part of the preparation. Let’s make sure everything is lined up!\",\"type\":\"summary_text\"},{\"text\":\"**Determining frontend skills**\\n\\nThe audit process requires running the frontend-design, and if context is missing, to involve teach-impeccable. However, we're not directly asked to do the audit. The user's request about aligning tokens and extracting reusable pieces points to skills like extract, normalize, and maybe colorize. Since the caveman skill must always be included, I need to ensure I list it on the \\\"Why\\\" line. To keep things succinct, I’ll likely go with extract, normalize, and colorize for the skills to load. Let's check the skill files to confirm their capabilities!\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/extract/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":5863,"output":1350,"cacheRead":2560,"cacheWrite":0,"totalTokens":9773,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775940989982,"responseId":"FL+gsVT1reycKksFn6MwxU8Zt1kXl8RfSVR2kbr3nHKtHqyh9cymZonkzEgQAwjBAhxpUUBmTK1VWhm2O35JUktvx17GredWRNNgOqwNlFXDHiZQCOC06P+mxMLpqwhRjfD/mFjygllGs5G5wPl6ItILzJH4l5eTta685AD//vdB/tZxpuJl9/hnDlfQFXE8PROxIVY7zjCVZlsZkwGPgJgEAjGLnZGv8utqsrP4pBiBni67MotFnEW4lsFB+zlt9T5EkbNqg+QQjA1b+GLr3wHY5i3ykCvN6O3m8iiKCHRoQ5o6tgJPE0wYRTnfx62nx1+BQxf6llIBQS1gyPT3oglAiiZKCw6q2qUlqJDcNU4edrx3DAAdciWB1KD8gVlch9c9jNLAJI2ekPUjmTaEaLh0jwzYpRbZzw=="},{"role":"toolResult","toolCallId":"call_W9Em2rdyV6Yd2RDiftNfG7sy|zm6JfUanu1ea19wOczpwK6NinH2FPOe9BerxStOOFRY/2JoIVYg0ZD1CU9kB4BF4h2USUbyHHUZB3emlPePzgFWCD51pgvvydcdTp1I+75sWpiz9NSwjCrSZNXEJQA7PaP2XS/D+a7+r+ZYNcW0qwtjloWPQjYy4oPHFqMxIn+Ysjq1Db4XPgi85pV1p1xyNvbJN07RoSCeHq7oFI3Yz+Jg/EB5lWkno01eLBmbMGF83z26zexSq0Y/h+KqvOWEZHMw9nZBCrTowIHwS7pR/Ex3M5CfGLx2VFtDxBYuukG1PzzUb+6v0w0W+6CSkuqcxdz5G+q8Xj/ySnoVBqBH0BmSOoivPc4g0eooC0+0fLulmyOpY/vWVjax6VxTkMOtz4jjrzdwq4AmDKRva4n3zBUOXILP7TJ0=","toolName":"read","content":[{"type":"text","text":"---\nname: extract\ndescription: Extract and consolidate reusable components, design tokens, and patterns into your design system. Identifies opportunities for systematic reuse and enriches your component library. Use when the user asks to create components, refactor repeated UI patterns, build a design system, or extract tokens.\n---\n\nIdentify reusable patterns, components, and design tokens, then extract and consolidate them into the design system for systematic reuse.\n\n## Discover\n\nAnalyze the target area to identify extraction opportunities:\n\n1. **Find the design system**: Locate your design system, component library, or shared UI directory (grep for \"design system\", \"ui\", \"components\", etc.). Understand its structure:\n   - Component organization and naming conventions\n   - Design token structure (if any)\n   - Documentation patterns\n   - Import/export conventions\n   \n   **CRITICAL**: If no design system exists, ask before creating one. Understand the preferred location and structure first.\n\n2. **Identify patterns**: Look for:\n   - **Repeated components**: Similar UI patterns used multiple times (buttons, cards, inputs, etc.)\n   - **Hard-coded values**: Colors, spacing, typography, shadows that should be tokens\n   - **Inconsistent variations**: Multiple implementations of the same concept (3 different button styles)\n   - **Reusable patterns**: Layout patterns, composition patterns, interaction patterns worth systematizing\n\n3. **Assess value**: Not everything should be extracted. Consider:\n   - Is this used 3+ times, or likely to be reused?\n   - Would systematizing this improve consistency?\n   - Is this a general pattern or context-specific?\n   - What's the maintenance cost vs benefit?\n\n## Plan Extraction\n\nCreate a systematic extraction plan:\n\n- **Components to extract**: Which UI elements become reusable components?\n- **Tokens to create**: Which hard-coded values become design tokens?\n- **Variants to support**: What variations does each component need?\n- **Naming conventions**: Component names, token names, prop names that match existing patterns\n- **Migration path**: How to refactor existing uses to consume the new shared versions\n\n**IMPORTANT**: Design systems grow incrementally. Extract what's clearly reusable now, not everything that might someday be reusable.\n\n## Extract & Enrich\n\nBuild improved, reusable versions:\n\n- **Components**: Create well-designed components with:\n  - Clear props API with sensible defaults\n  - Proper variants for different use cases\n  - Accessibility built in (ARIA, keyboard navigation, focus management)\n  - Documentation and usage examples\n  \n- **Design tokens**: Create tokens with:\n  - Clear naming (primitive vs semantic)\n  - Proper hierarchy and organization\n  - Documentation of when to use each token\n  \n- **Patterns**: Document patterns with:\n  - When to use this pattern\n  - Code examples\n  - Variations and combinations\n\n**NEVER**:\n- Extract one-off, context-specific implementations without generalization\n- Create components so generic they're useless\n- Extract without considering existing design system conventions\n- Skip proper TypeScript types or prop documentation\n- Create tokens for every single value (tokens should have semantic meaning)\n\n## Migrate\n\nReplace existing uses with the new shared versions:\n\n- **Find all instances**: Search for the patterns you've extracted\n- **Replace systematically**: Update each use to consume the shared version\n- **Test thoroughly**: Ensure visual and functional parity\n- **Delete dead code**: Remove the old implementations\n\n## Document\n\nUpdate design system documentation:\n\n- Add new components to the component library\n- Document token usage and values\n- Add examples and guidelines\n- Update any Storybook or component catalog\n\nRemember: A good design system is a living system. Extract patterns as they emerge, enrich them thoughtfully, and maintain them consistently."}],"isError":false,"timestamp":1775941019894},{"role":"assistant","content":[{"type":"toolCall","id":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/normalize/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":7995,"output":33,"cacheRead":2560,"cacheWrite":0,"totalTokens":10588,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775941019899,"responseId":"PwPfxd9e/VNsG8BnFxFvR7HluyCziWk8/gAoxe40kwsL57dW6hGmABzppW+jZMo81nsU7LituNndEhg/2X6Uv+cRTlFe87yrlCVadJmex8EheMklL9NeYnlNCljyrL70QYFsTADa7RPWrlBqJl0eQOOfDAoR9shdmBrdkx/I3qWx7CPFO5vPaCM/kvKSTIRoCspg45x+gWfmdR1DIyp25R/cRxdqNYjyfdJt+sPDLTJbOX/uWenT/fSXmRsVl4qhpItfkvDG2oQtCbXs5X9i713/IDDozcABqC/vGymiVDv22PVmMZlcatxoQ9jBakNQO4SKJhG4Mob3CXEfs0gKQI4f3ocrrnSbttLOeZ+dk5+PcqXoIEgBE9v5EjUrtq1XZMNZ3pjQNoSxIta2EAxnI/5Z8T47cEJGeg=="},{"role":"toolResult","toolCallId":"call_XjGBlTCFx2XtpKBWY1W7tB2R|uUD2PG55JoM4DZJKU4Or6rdazIBeRK27v3NTsbuxcChbJDhwIjzKlezLZnZN0+2LHDH/duYir52Ctq1FnuBktQb836GC2HTYGi6pBCgLlGD/CMEXKtfmkzhW/WwEp83pLxibrl9WPCfYyFnMRNE1BcsyPBMhMfrMyuT0bYeqmPNfWNm/ZHp3oW3qgrM9LJyZTGDBqdmAQp710pnVnOUciBHApQXqN8of98mlyWVOqXFBHwmCXHajhCSPFgUB7depUM3AFT72mIac2L8VU3Xw6uwHPEcOZZgGZIpjhMBVJ30p05b+iM7SrvDyI6t/kqxBng2tQtpOzDs7JbPGgi2hGNg22PaJx5W1dOh3zLyIumi/SwjQmwh99YZz876jXDSz346PlEoaLST8L3tVjCRfn58rnhrH1Sk=","toolName":"read","content":[{"type":"text","text":"---\nname: normalize\ndescription: Audits and realigns UI to match design system standards, spacing, tokens, and patterns. Use when the user mentions consistency, design drift, mismatched styles, tokens, or wants to bring a feature back in line with the system.\n---\n\nAnalyze and redesign the feature to perfectly match our design system standards, aesthetics, and established patterns.\n\n## MANDATORY PREPARATION\n\nInvoke /frontend-design — it contains design principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first.\n\n---\n\n## Plan\n\nBefore making changes, deeply understand the context:\n\n1. **Discover the design system**: Search for design system documentation, UI guidelines, component libraries, or style guides (grep for \"design system\", \"ui guide\", \"style guide\", etc.). Study it thoroughly until you understand:\n   - Core design principles and aesthetic direction\n   - Target audience and personas\n   - Component patterns and conventions\n   - Design tokens (colors, typography, spacing)\n   \n   **CRITICAL**: If something isn't clear, ask. Don't guess at design system principles.\n\n2. **Analyze the current feature**: Assess what works and what doesn't:\n   - Where does it deviate from design system patterns?\n   - Which inconsistencies are cosmetic vs. functional?\n   - What's the root cause—missing tokens, one-off implementations, or conceptual misalignment?\n\n3. **Create a normalization plan**: Define specific changes that will align the feature with the design system:\n   - Which components can be replaced with design system equivalents?\n   - Which styles need to use design tokens instead of hard-coded values?\n   - How can UX patterns match established user flows?\n   \n   **IMPORTANT**: Great design is effective design. Prioritize UX consistency and usability over visual polish alone. Think through the best possible experience for your use case and personas first.\n\n## Execute\n\nSystematically address all inconsistencies across these dimensions:\n\n- **Typography**: Use design system fonts, sizes, weights, and line heights. Replace hard-coded values with typographic tokens or classes.\n- **Color & Theme**: Apply design system color tokens. Remove one-off color choices that break the palette.\n- **Spacing & Layout**: Use spacing tokens (margins, padding, gaps). Align with grid systems and layout patterns used elsewhere.\n- **Components**: Replace custom implementations with design system components. Ensure props and variants match established patterns.\n- **Motion & Interaction**: Match animation timing, easing, and interaction patterns to other features.\n- **Responsive Behavior**: Ensure breakpoints and responsive patterns align with design system standards.\n- **Accessibility**: Verify contrast ratios, focus states, ARIA labels match design system requirements.\n- **Progressive Disclosure**: Match information hierarchy and complexity management to established patterns.\n\n**NEVER**:\n- Create new one-off components when design system equivalents exist\n- Hard-code values that should use design tokens\n- Introduce new patterns that diverge from the design system\n- Compromise accessibility for visual consistency\n\nThis is not an exhaustive list—apply judgment to identify all areas needing normalization.\n\n## Clean Up\n\nAfter normalization, ensure code quality:\n\n- **Consolidate reusable components**: If you created new components that should be shared, move them to the design system or shared UI component path.\n- **Remove orphaned code**: Delete unused implementations, styles, or files made obsolete by normalization.\n- **Verify quality**: Lint, type-check, and test according to repository guidelines. Ensure normalization didn't introduce regressions.\n- **Ensure DRYness**: Look for duplication introduced during refactoring and consolidate.\n\nRemember: You are a brilliant frontend designer with impeccable taste, equally strong in UX and UI. Your attention to detail and eye for end-to-end user experience is world class. Execute with precision and thoroughness."}],"isError":false,"timestamp":1775941021898},{"role":"assistant","content":[{"type":"toolCall","id":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","name":"read","arguments":{"path":"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md"},"partialJson":"{\"path\":\"/home/alex/.pi/agent/skills/frontend/colorize/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":8839,"output":33,"cacheRead":2560,"cacheWrite":0,"totalTokens":11432,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775941021907,"responseId":"Aq+BnWHQ7UpSsE/HIO2ehWfSQRgUtv6Zry718Sb3EkjoMbUV7XECcMyJGwDbiZkBYv602c6H1ko9lRT0hSo8AwR09i8OrAkP/JyyMbNpiuDpSKrNsRd9nOYpLSXaZJ8fbFNX6VZCtmQ/9UKea+xECsf1tBE9sGQWuRU8fBDqLe4iX3VCFX2hynwDeDv8iQFlvKTYRvUTRI8p0EGy6DwYXhYw9DNuMQiAxRZKGqU40+laK03vocl8u2UavN16d1uk7TCyQ0J9UoJ4zuDFd+JfI9Tf8ZQxhGrmVrN447TakhmnE4JfcH997g8jszqktYhY1d+/u4ysYWwAy31UrWDYdpXhWKV4gBBSm2xItCClnE6xxmlxeByvYMv1EUAR2dxP3uxp1dLdjKeTBVIjTc9YkL4rgngAs6P37A=="},{"role":"toolResult","toolCallId":"call_m0QdTLVdosVNn6MKhTZNNRly|xLgLPTsaKznGJQDskEBISNJX4lr60TVerVNGECC8HfNe2WMlOAXDQI7dLjY12xKYhNYLGcbMcPrlenvFSsLlEyXZFkolMLpCH8eObmD5GwobjY2y9xv8Uaofn0k/48HIotOwP7xPN6PkZ7LpBtF5WbaHFKqGd29/A1PahZHxfDVckH5ryjh6VLH1r4vRK9+e/sAUd1nRMSaUaliEX7zIH7+Mb2dRtHH8HBSlOuXJMEbuqNsN4LS68a7xJ73iVBujc/solEiS4Bm6+lFhOlnOAaWbOzF9ZMuoYx1YMQys+mPKoHl1nxTrgJd1B7YYYQ0Ii7dU7TaSBIbSHTuUMOAWVY1BlXSYVnsmRaJsrvjmA53bXJj4GdBtmQhZqEOvcpMTcyLCm4mg/ZmCFiTqW3fEJWVUlM4CDXk=","toolName":"read","content":[{"type":"text","text":"---\nname: colorize\ndescription: Add strategic color to features that are too monochromatic or lack visual interest, making interfaces more engaging and expressive. Use when the user mentions the design looking gray, dull, lacking warmth, needing more color, or wanting a more vibrant or expressive palette.\n---\n\nStrategically introduce color to designs that are too monochromatic, gray, or lacking in visual warmth and personality.\n\n## MANDATORY PREPARATION\n\nInvoke /frontend-design — it contains design principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first. Additionally gather: existing brand colors.\n\n---\n\n## Assess Color Opportunity\n\nAnalyze the current state and identify opportunities:\n\n1. **Understand current state**:\n   - **Color absence**: Pure grayscale? Limited neutrals? One timid accent?\n   - **Missed opportunities**: Where could color add meaning, hierarchy, or delight?\n   - **Context**: What's appropriate for this domain and audience?\n   - **Brand**: Are there existing brand colors we should use?\n\n2. **Identify where color adds value**:\n   - **Semantic meaning**: Success (green), error (red), warning (yellow/orange), info (blue)\n   - **Hierarchy**: Drawing attention to important elements\n   - **Categorization**: Different sections, types, or states\n   - **Emotional tone**: Warmth, energy, trust, creativity\n   - **Wayfinding**: Helping users navigate and understand structure\n   - **Delight**: Moments of visual interest and personality\n\nIf any of these are unclear from the codebase, ask the user directly to clarify what you cannot infer.\n\n**CRITICAL**: More color ≠ better. Strategic color beats rainbow vomit every time. Every color should have a purpose.\n\n## Plan Color Strategy\n\nCreate a purposeful color introduction plan:\n\n- **Color palette**: What colors match the brand/context? (Choose 2-4 colors max beyond neutrals)\n- **Dominant color**: Which color owns 60% of colored elements?\n- **Accent colors**: Which colors provide contrast and highlights? (30% and 10%)\n- **Application strategy**: Where does each color appear and why?\n\n**IMPORTANT**: Color should enhance hierarchy and meaning, not create chaos. Less is more when it matters more.\n\n## Introduce Color Strategically\n\nAdd color systematically across these dimensions:\n\n### Semantic Color\n- **State indicators**:\n  - Success: Green tones (emerald, forest, mint)\n  - Error: Red/pink tones (rose, crimson, coral)\n  - Warning: Orange/amber tones\n  - Info: Blue tones (sky, ocean, indigo)\n  - Neutral: Gray/slate for inactive states\n\n- **Status badges**: Colored backgrounds or borders for states (active, pending, completed, etc.)\n- **Progress indicators**: Colored bars, rings, or charts showing completion or health\n\n### Accent Color Application\n- **Primary actions**: Color the most important buttons/CTAs\n- **Links**: Add color to clickable text (maintain accessibility)\n- **Icons**: Colorize key icons for recognition and personality\n- **Headers/titles**: Add color to section headers or key labels\n- **Hover states**: Introduce color on interaction\n\n### Background & Surfaces\n- **Tinted backgrounds**: Replace pure gray (`#f5f5f5`) with warm neutrals (`oklch(97% 0.01 60)`) or cool tints (`oklch(97% 0.01 250)`)\n- **Colored sections**: Use subtle background colors to separate areas\n- **Gradient backgrounds**: Add depth with subtle, intentional gradients (not generic purple-blue)\n- **Cards & surfaces**: Tint cards or surfaces slightly for warmth\n\n**Use OKLCH for color**: It's perceptually uniform, meaning equal steps in lightness *look* equal. Great for generating harmonious scales.\n\n### Data Visualization\n- **Charts & graphs**: Use color to encode categories or values\n- **Heatmaps**: Color intensity shows density or importance\n- **Comparison**: Color coding for different datasets or timeframes\n\n### Borders & Accents\n- **Accent borders**: Add colored left/top borders to cards or sections\n- **Underlines**: Color underlines for emphasis or active states\n- **Dividers**: Subtle colored dividers instead of gray lines\n- **Focus rings**: Colored focus indicators matching brand\n\n### Typography Color\n- **Colored headings**: Use brand colors for section headings (maintain contrast)\n- **Highlight text**: Color for emphasis or categories\n- **Labels & tags**: Small colored labels for metadata or categories\n\n### Decorative Elements\n- **Illustrations**: Add colored illustrations or icons\n- **Shapes**: Geometric shapes in brand colors as background elements\n- **Gradients**: Colorful gradient overlays or mesh backgrounds\n- **Blobs/organic shapes**: Soft colored shapes for visual interest\n\n## Balance & Refinement\n\nEnsure color addition improves rather than overwhelms:\n\n### Maintain Hierarchy\n- **Dominant color** (60%): Primary brand color or most used accent\n- **Secondary color** (30%): Supporting color for variety\n- **Accent color** (10%): High contrast for key moments\n- **Neutrals** (remaining): Gray/black/white for structure\n\n### Accessibility\n- **Contrast ratios**: Ensure WCAG compliance (4.5:1 for text, 3:1 for UI components)\n- **Don't rely on color alone**: Use icons, labels, or patterns alongside color\n- **Test for color blindness**: Verify red/green combinations work for all users\n\n### Cohesion\n- **Consistent palette**: Use colors from defined palette, not arbitrary choices\n- **Systematic application**: Same color meanings throughout (green always = success)\n- **Temperature consistency**: Warm palette stays warm, cool stays cool\n\n**NEVER**:\n- Use every color in the rainbow (choose 2-4 colors beyond neutrals)\n- Apply color randomly without semantic meaning\n- Put gray text on colored backgrounds—it looks washed out; use a darker shade of the background color or transparency instead\n- Use pure gray for neutrals—add subtle color tint (warm or cool) for sophistication\n- Use pure black (`#000`) or pure white (`#fff`) for large areas\n- Violate WCAG contrast requirements\n- Use color as the only indicator (accessibility issue)\n- Make everything colorful (defeats the purpose)\n- Default to purple-blue gradients (AI slop aesthetic)\n\n## Verify Color Addition\n\nTest that colorization improves the experience:\n\n- **Better hierarchy**: Does color guide attention appropriately?\n- **Clearer meaning**: Does color help users understand states/categories?\n- **More engaging**: Does the interface feel warmer and more inviting?\n- **Still accessible**: Do all color combinations meet WCAG standards?\n- **Not overwhelming**: Is color balanced and purposeful?\n\nRemember: Color is emotional and powerful. Use it to create warmth, guide attention, communicate meaning, and express personality. But restraint and strategy matter more than saturation and variety. Be colorful, but be intentional."}],"isError":false,"timestamp":1775941024473},{"role":"assistant","content":[{"type":"thinking","thinking":"**Determining skill loading order**\n\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\n\n**Selecting skills and order**\n\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\n\n**Choosing frontend skills**\n\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\n\n**Selecting frontend skills**\n\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \"Skills: frontend-design, extract, normalize.\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\n\n**Finalizing skill selection output**\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design → extract → normalize  \nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"raGrkyRx9+Jxq6tcgkFrdrMSvvyk2ZsvuJkjNAsWHITyBgt21OzANC+AtCH6dLfA88mCjsQnDAFWKBe/+5IFSo7k6bfBUS+KDydnZu8CwAxOVAAjLkCpnhzJE8dW3OJSk2ypYbUfj42UeC0Pa+7Jc+1F95XFFFXdRnCxX6QqNb55SXKiuoTEWtSLcAq5ai9FvsNNcaIiS3diPcc4hF8OVyY7pyYiFS9npTRXewpWvYFqSdoNolbD18nG35lAaPJrSESZCYuRHUjzPvbDJWKxQSH477slbUuHtR5RmSpPI4uSaoA3QUVoNYDRYKcYLprTomyBV51XE61ZJmI6nlA4/6HNOZxiqXpBPZgxBC9M1TEHbifAPFcVJiizxbDjysKrkX8FM90Dl6F/JSc4pcuJ5QfmsXKmHG4=\",\"summary\":[{\"text\":\"**Determining skill loading order**\\n\\nThe user is asking which frontend skills to load from a directory: frontend-design, extract, and normalize. I need to pick an order based on dependencies. It seems I must follow the frontend-design's Context Gathering Protocol first. Extract doesn't need frontend-design, but normalize does. After gathering context and brand colors with frontend-design, I think I should run extract next to identify components, followed by normalize. I must also consider the possibility of needing to run teach-impeccable first if required. The order would likely be frontend-design, teach-impeccable, extract, then normalize.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills and order**\\n\\nThe user asked me to choose skills to load, and I think I need to include teach-impeccable if the design context is missing. The task is about extracting reusable pieces and bringing things back into alignment. So, the correct selection should be: frontend-design, extract, normalize. The order is: 1) frontend-design, 2) extract, 3) normalize. This is because I need to gather context and brand colors first and then identify components to align and replace.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user asked me to choose which frontend skills to load from a specific directory. I need to follow the project-specific instructions, which say to always load the caveman skill but focus only on frontend skills in this scenario. So, I’ll stick to the instructions and select only those relevant skills. Since caveman isn't in the frontend directory, I will not include it. My final skills selection will be concise and limited to the frontend options available.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nI need to adhere to the user’s instructions, which require me to select the frontend skills to load. I should invoke frontend-design first, as it contains essential design principles and the Context Gathering Protocol. Since the user is focusing on skill selection, I’ll keep it minimal. The final output will say: \\\"Skills: frontend-design, extract, normalize.\\\" Then, I’ll include the exact third line for clarity, conforming to the requirement of three precise lines.\",\"type\":\"summary_text\"},{\"text\":\"**Finalizing skill selection output**\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design → extract → normalize  \\nWhy: Gather design context & brand colors first (teach-impeccable if missing), then identify and extract repeated card patterns/tokens, then normalize code to use tokens, consolidate components, and remove drift.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design → extract → normalize\nWhy: frontend-design gathers design context & brand colors (run teach-impeccable if missing); extract finds/repackages repeated card patterns and token candidates; normalize replaces hard-coded values with tokens, consolidates components, and aligns the UI with the design system.","textSignature":"{\"v\":1,\"id\":\"E+erobMPty3kFLmPhy4iafDX6i3IHQ0XdjemsrB7TARqzNcGA90UZTh2SmajRxqdA5MpSErVQgF8AIbkQudeGkaovjv0DOs4cz9IXMJsSgQPKZxwLxE2MKoWTBdW/7lk12Gb0vAyFBMS3XqXYNaPSWAcv8yoSMdji762M8EwGQEgJScjJjEP0BwPSJy4Ur1Je1FO9ZnlDK9ljkzZgjc9SUZCAiegviVfU2InZWpEchjXj1pvJoPpc6sJzr6LolZCVDKawQrU+pBPldNaGfFy5woj0jXsaY0VeL6tilRTd5MB57RY1LQHzXmYOp8tZ7ofTTuyWZjUi2NmS0QstZWpMLSFit4RamLtsYj1ier/FnTjTQpEzHZyWsqNePxIVmuSBG1S0y3mMndg6HJfWnkkY9TjL++HkL+3\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":10392,"output":2136,"cacheRead":2560,"cacheWrite":0,"totalTokens":15088,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941024482,"responseId":"+O1dr0TuGM4DjxUtjTzxQbsiWBsXFhu38qigPh3gpvbstabxZNuSj+rgrehRVZzN0RzEAGXxKR6UKDpRriG2Dfd85PIpQTA7z56S6E0MsenuOuoJi9GN+1f9PaFHJdREXpLJ7/7Kb3Qwe/frRseDyqxYTBW15l7PQj8LTN1JACl53hCnoCwTkM1WKmGrMjB+SCdwQCKFAgaCosLCTLtGBGhe6mroFg5jl0J+yXOLKZyMRQ6xBrIUwPgfEOWz/GjBk9c81ieZXX8B/3mi235SX9j+VSCLHhKgf9jSioCIF8HwMW64+9CwlTdRVozPoLd9t8dnJgSQBP0ZRIldrF6Nuzg5k03CsCyeXSTESgdG4QwZdFmkoWPWesBxCKcEBBUlGfwNaV6Q/BR9xLXcgwwKA3HAVaaAF74LPg=="}]}